Conversational Recommender Systems (CRS) aim to provide personalized recommendations by interacting with users through natural language dialogue. However, in scenarios requiring deep geospatial awareness, existing methods, including those based on Large Language Models (LLMs), still face significant challenges in effectively fusing heterogeneous, multimodal geographic information with dynamic dialogue context. Simple fusion strategies struggle to resolve the asymmetric dependencies between dynamic user intent and static geographic context and fail to bridge the semantic gap between LLMs and structured geospatial data. To address these issues, we propose a framework for geography-aware CRS, named GeoCRS. Our core idea is to empower a frozen LLM with powerful geospatial reasoning capabilities by conditioning it on a dynamic, multimodal guidance signal generated by an external fusion architecture, all without altering the LLM's internal parameters. Specifically, we first design a hierarchical geographical encoder to uniformly represent heterogeneous geographic data. Subsequently, we introduce a contextual feature modulation module that asymmetrically injects the geographic context into the user's dialogue intent via a novel modulation mechanism to improve conversational recommendation via both geographic and dialogue context. Extensive experiments on public benchmark datasets demonstrate that our proposed GeoCRS significantly outperforms state-of-the-art baselines on the geography-aware conversational recommendation task.

Multi-View Clustering (MVC) is a pivotal multi-view learning paradigm widely adopted across various fields. Despite recent advances, existing methods primarily focus on enhancing the performance of fused multi-view representation, often neglecting the issue of Representation Degradation (RD) arising from discrepancies in the intrinsic quality of different views. To address the limitations, we propose a novel Granular-ball Fuzzy Split and Attention Fusion (GFSAF) learning, which leverages the nature of granular-ball to extract mutual and complementary representation separately. Meanwhile, the proposed method introduces an attention variant for fused representations to mitigate the RD issue. GFSAF mainly consists of two training stages: Split-Extract Stage and Views-Fusion Stage. Specifically, we design a novel Granular-ball Fuzzy Contrastive Learning to extract mutual representation, and introduce Noise Stripping Loss to reduce the influence of noise for complementary representation. Then, a novel multi-head Cross Views Attention is proposed to employ attention mechanism from multi-view perspectives for comprehensive fused representations. Experimental results on eight databases demonstrate that our GFSAF achieves superior performance compared to several state-of-the-art MVC methods.

Fully fine-tuning large pre-trained models for each downstream task is impractical due to prohibitive memory, computation, and storage costs. Although parameter-efficient fine-tuning (PEFT) methods address this issue, leading methods like LoRA still exhibit linear scaling of trainable parameters with hidden size. Recent studies have explored PEFT in the frequency domain to reduce computational costs by employing fast Fourier transform and discrete cosine transform with sparse frequency selection. These methods rely on global frequency representations that lack spatial locality and disperse energy across the domain. As a result, sparse coefficient selection struggles to preserve fine-grained structural information and often introduces artifacts such as ringing near boundaries. To address these limitations, we propose DWTSG, a novel PEFT framework based on discrete wavelet transform (DWT) and subband guidance. DWTSG decomposes pre-trained weights into four wavelet subbands that jointly encode global context and local details. It fine-tunes only the most informative coefficients in each subband through an energy-based selection strategy that prioritizes coefficients based on their individual importance and interactions. Finally, inverse DWT reconstructs the updated weights, enabling efficient and precise adaptation. Extensive experiments on natural language understanding, commonsense reasoning, and image classification demonstrate that DWTSG outperforms existing PEFT methods, achieving superior performance and higher parameter efficiency.

Reflective imaging enables the mirror imagings and physical entities to possess identical attributes, e.g., color and shape. Current mirror detection (MD) methods primarily rely on designing functional components to establish the correlation and disparities between the imagings and entities, thereby identifying the mirror regions. However, the exploration of extended scenes with dynamic content changes is rarely investigated. Therefore, we propose the MirrorSAM designed for MD based on the Segment Anything Model (SAM). Specifically, due to the varying reflections produced by mirrors in different positions and the complex visual space that interferes with localization, we design the hierarchical mixture of direction experts (HMDE) in the low-rank space to reduce biases towards entities in SAM and dynamically adjust experts based on the input scene. We observe differences in depth between mirrors and adjacent areas, and propose the depth token calibration (DTC), which introduces a learnable depth token to generate the depth map and serve as an error correction factor. We further formulate the selective pixel-prototype contrastive (SPPC) loss, selecting partially confusable samples to promote the decoupling of mirror and non-mirror representations. Extensive experiments conducted on four mirror benchmarks and two settings demonstrate that our approach surpasses state-of-the-art methods with few trainable parameters and FLOPs. We further extend to four transparent surface benchmarks to validate generalization.

Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Session-based recommendation aims to predict the next item that anonymous users may be interested in, based on their current session interactions. Recent studies have demonstrated that retrieving neighbor sessions to augment the current session can effectively alleviate the data sparsity issue and improve recommendation performance. However, existing methods typically rely on explicitly observed session data, neglecting latent neighbors - not directly observed but potentially relevant within the interest space - thereby failing to fully exploit the potential of neighbor sessions in recommendation.

Large Language Models (LLMs) have demonstrated strong potential in recommendations due to their powerful reasoning capabilities. However, existing methods typically rely on a single reasoning path to drive the entire Top-K recommendations. This paradigm is prone to reasoning path collapse, where limiting exploration of potentially superior and diverse reasoning paths within the LLMs space. As a result, both the accuracy and diversity of the recommendation outcomes are constrained.

Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East–focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting tasks. The code is available at https://github.com/LuminosityX/ThinkTank-ME.

Large language models (LLMs) have emerged as a promising paradigm for recommender systems, due to their powerful capabilities in global knowledge integration and reasoning. However, LLMs are inherently prone to confirmation bias -- the tendency to favor information that reinforces users' existing views -- which leads to an overemphasis on previously shown viewpoints and ignores diverse user beliefs for recommendations. To address this issue, in this paper, we propose SCoTRec, a social chain-of-thought reasoning framework for recommendation. SCoTRec first constructs sentiment-aware user profiles by extracting sentiment terms from user reviews. It then incorporates users' social sentiment information into the social chain-of-thought reasoning units to improve recommendations. In particular, we categorize the social chain-of-thought into sentiment-based pathways and apply human evaluation operations -- backtracking, discarding, retaining, and aggregating -- to simulate nuanced sentiment cognition and interpersonal influence, effectively alleviating confirmation bias. Extensive experiments on four benchmark datasets demonstrate the effectiveness of SCoTRec in alleviating confirmation bias and improving recommendations.

Conversational recommender systems (CRSs) aim to provide real-time recommendations through dynamic interactions between users and the system. Recent studies have revealed the value of personalized information derived from users' historical dialogue records in refining user preferences. However, existing methods often utilize the entire historical dialogue of a user indiscriminately, leading to the issue of cognitive negative transfer, wherein historical dialogue sessions impede rather than facilitate current decision-making. This ultimately degrades the performance of conversational recommendations.

Eliminating semantic discrepancy between different modalities is the ultimate goal of image text retrieval. However, most of the existing methods only focus on retrieval of the ground-truth instance while ignoring those semantically similar instances yet unlabeled as positives, which causes the phenomenon of one-to-many correspondence. The mainstream solutions of this research are mainly based on uncertainty learning and the exploration of one-to-many correspondence is still insufficient albeit their significant progress. Therefore, this work develops a novel Distribution-to-Points (termed D2P) matching mechanism for image-text retrieval to capture the one-to-many correspondence between multiple samples and a given query via hypergraph modeling. Specifically, a given query is first mapped as a probabilistic embedding to learn its true semantic distribution based on Mahalanobis distance. Then each candidate instance in a mini-batch is regarded as a hypergraph node with its mean semantics while a Gaussian query is modeled as a hyperedge to capture the semantic correlations beyond the pair between candidate points and the query. Moreover, an energy-based semantic modeling framework is developed to pull all similar candidates (not only the ground truth) close to their query while pushing those dissimilar ones far away. In the end, distribution-to-points matching is learned based on the similarity measurement over the Mahalanobis distance, which considers semantic variance to perform many-to-one correspondence well. Experimental results on several widely used datasets and under various evaluation metrics confirm our superiority and effectiveness in improving the retrieval ability of the baseline including ground-truth matching and semantic multiplicity for image text retrieval.

Mirror detection (MD) aims to overcome interference caused by reflections and locate mirror regions. Existing methods focus on designing components to explicitly establish the associations between physical entities and corresponding imagings, or utilizing rotation to construct symmetric consistency. We observe that: a) incomplete and incorrect correspondence between entities and imagings; b) other physical materials (e.g., glass) exhibit characteristics partially similar to mirrors, causing confusion when they co-occur; c) complex interfering factors (e.g., occlusion) and reflection mechanisms may expand vector space several times over. To address these issues in a unified manner, we formulate the scene-aware visual reasoning network (SVRNet) based on visual prompts. Specifically, we construct the prototype-guided prompt chain reasoning (PPCR) that generates a mixed chain of thought reasoning based on maximal difference heterogeneous prototypes to construct comprehensive spatial location and semantic perception. Noise may accumulate gradually through the chain, and crucial clues may also disappear. Therefore, we design the prompt evolution (PE) to filter out noise and enhance the coupling between prompts. We further develop the mixture of prompt injection expert (MPIE) to dynamically select the optimal injection strategy in the low-rank space based on specific scene. Due to reflection interference and random parameter space introducing potential ambiguity, we formulate the three-way evidence-aware (TEA) loss to quantify the uncertainty, thereby providing reliable predictions. To leverage historical knowledge and further disentangle representations, we propose the frequency prototype contrastive (FPC) loss for learning more generalizable features across images. Finally, we relabel 25,828 images and formulate the first point-supervised MD framework. Extensive experiments conducted on four mirror benchmarks under three settings demonstrate that our method surpasses state-of-the-art approaches. Promising results are also achieved on six related benchmarks, showing its generality.

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, but their deployment in resource-constrained environments remains challenging due to substantial memory and computational requirements. Benefiting from the sparse event-driven computation paradigm of Spiking Neural Networks (SNNs), some research has focused on designing spike-based language models. However, existing spike-based language models achieve only partial computational efficiency gains and fail to address memory constraints comprehensively. In this paper, we propose an evolved and quantized spike-driven language model (EQ-SpikeLM) to address identified challenges. This model incorporates two primary innovations. First, inspired by the artificial bee colony algorithm in evolutionary computation, we propose an architecture evolution method, namely ABC-Arc. This method optimizes network topology by systematically removing redundant neural pathways. Second, a dynamic post-training quantization (DynPTQ) strategy is developed for the evolved SpikeLM, facilitating the conversion of floating-point parameters to lower-bit precision without requiring model retraining. By combining these two methods, EQ-SpikeLM significantly reduces storage and computational demands while preserving model performance. Experimental evaluation on the GLUE benchmark demonstrates EQ-SpikeLM’s ability to maintain performance equivalent to its uncompressed counterpart, with a substantial reduction in both model size and power consumption. These results position EQ-SpikeLM as a viable approach for deploying large language models in resource-constrained edge computing scenarios.

Action Quality Assessment (AQA) has gained significant attention due to its potential real-world applications, which require a fine-grained understanding of action sequences. Recent works have attempted to utilize multimodal video features and address some existing challenges. However, these approaches primarily focus on leveraging textual information from language models only, leading to instability and suboptimal performance due to directional bias in a vision-language joint embedding space. To tackle these issues, we propose a Vision-Language Collaboration Representation Learning approach (VLC-Net) to understand fine-grained action sequences and create a unified feature representation along with their temporal dependencies for accurate AQA score prediction. Specifically, we design a bidirectional knowledge distillation operation to perform collaboration learning between vision-language pre-trained knowledge and visual action knowledge for fine-grained action feature learning. Furthermore, we design vision-language alignment guidance to explicitly align action features with the same action semantics across modalities, thereby unifying their joint representation. Leveraging these aligned features, we propose multimodal contrastive learning to relate modalities and align subactions with textual descriptions, ensuring accurate action representation. We conduct experiments on the FineDiving, MTL-AQA, FineFS, and Fis-V datasets, demonstrating the effectiveness of our approach, which outperforms state-of-the-art methods.

Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes. Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and annotation dependency, limiting their adaptability to scene variations and unseen camouflage patterns. To overcome these, we propose the hierarchical consistency learning (HCL) framework, which integrates test-time adaptation for dynamic representation recalibration. Specifically, we design the hierarchical representation reconstruction (HRR) to alleviate feature entanglement by synergizing spatial reconstruction with dual-stream frequency-domain decomposition, enhancing robustness against appearance homogenization. The pixel and spectrum inference provide structural and contextual priors. We further introduce task affinity guidance (TAG) to propagate knowledge across branches via channel-wise affinity, aligning local discriminative cues and mitigating semantic drift. To ensure semantic invariance, we formulate the prototype consistency calibration (PCC), which aggregates region features into compact prototypes and establishes prototype-feature similarity. This imposes implicit and hierarchical constraints that bridge task and representation gaps. Extensive experiments across four camouflaged and four underwater object benchmarks, under three degradation settings, demonstrate that our method consistently outperforms state-of-the-art approaches, highlighting its robustness and generalization under distribution shifts.

Unsupervised cross-domain image retrieval (UCDIR) aims to obtain images with the same category from other domains yet without labels for all data. The prevailing strategy first enhances the intra-domain representation by clustering, and then realizes cross-domain alignment based on various similarity comparisons. Despite their significant advancements, current methods are hindered by substantial ambiguity resulting from the absence of explicit semantic information, exacerbated by various sources of noise such as background interference and attribute composition. This difficulty can compromise the performance of existing clustering-based methods. Therefore, we propose a straightforward yet effective strategy to Progressively Alleviate Noise (termedPAN) for UCDIR. We initially utilize information entropy to quantify the certainty of prediction, which also serves as an indicator of uncertainty level resulting from noise. We further refine a progressive learning approach that specifically targets data with low confidence to enhance the semantic comprehension. The model can gradually focus on data with appropriate uncertainty for semantic learning and cross-domain alignment via dynamic reweighing, thereby excluding the adverse effects of noisy data in an incremental manner. Extensive experiments on three widely used datasets for UCDIR demonstrate our effectiveness and superiority.

The rapid growth of model scale has necessitated substantial computational resources for fine-tuning. Existing approach such as low-rank adaptation (LoRA) has sought to address the problem of handling the large updated parameters in full fine-tuning (FT). However, LoRA utilize random initialization and optimization of low-rank matrices to approximate updated weights, which can result in suboptimal convergence and an accuracy gap compared to full fine-tuning (FT). To address these issues, we propose low-rank LDU (LoLDU), a parameter-efficient fine-tuning (PEFT) approach that significantly reduces trainable parameters by 2600 times compared to regular PEFT methods while maintaining comparable performance. LoLDU leverages lower-diag-upper (LDU) decomposition to initialize low-rank matrices for faster convergence and nonsingularity. We focus on optimizing the diagonal matrix for scaling transformations. To the best of our knowledge, LoLDU has the fewest parameters among all PEFT approaches. We conducted extensive experiments across 4 instruction-following datasets, six natural language understanding (NLU) datasets, eight image classification datasets, and image generation datasets with multiple model types [LLaMA2, RoBERTa, ViT, and stable diffusion (SD)], providing a comprehensive and detailed analysis. Our open-source code can be accessed at https://anonymous.4open.science/r/LoLDU-B5A6.

The Fourier transform (FT) stands as a fundamental tool in modern signal processing with widespread applications across various scientific and engineering fields. Therefore, there remains a need for continued research efforts to devise energy-efficient implementations of the FT. Due to their inherent energy efficiency, biologically plausible spiking neural networks (SNNs) emerge as a promising alternative solution. However, current SNN implementations of the FT suffer from two key shortcomings, namely, high latency and reduced accuracy. In this article, we analyze the underlying causes of these limitations and highlight deficiencies in the existing spike-based encoding mechanisms and spiking neuron models. We then propose a new SNN-based FT (SNN-FT) based on a logarithmically polarized time-to-first-spike (TTFS) encoding method (called LP-TTFS) along with a novel piecewise spiking neuron (PTSN) model based on ternary spikes (referred to as PTSN). The resulting SNN-FT is mathematically equivalent to the conventional FT and demonstrates superior performance in accuracy as well as reduced latency. We assess the performance of the proposed SNN-FT alternative through extensive experiments on FT-based applications, such as radar and audio signal processing, and the obtained results demonstrate the efficacy of SNN-FT and its superiority over the existing approaches. This study unveils a novel energy-efficient neuromorphic computing technique with great potential for FT applications across diverse scientific and engineering domains.

Offline reinforcement learning (RL) aims to learn effective agents from previously collected datasets, facilitating the safety and efficiency of RL by avoiding real-time interaction. However, in practical applications, the approximation error of the out-of-distribution (OOD) state-actions can cause considerable overestimation due to error exacerbation during training, finally degrading the performance. In contrast to prior works that merely addressed the OOD state-actions, we discover that all data introduces estimation error whose magnitude is directly related to data sparsity. Consequently, the impact of data sparsity is inevitable and vital when inhibiting the error exacerbation. In this article, we propose an offline RL approach to inhibit error exacerbation with data sparsity (IEEDS), which includes a novel value estimation method to consider the impact of data sparsity on the training of agents. Specifically, the value estimation phase includes two innovations: 1) replace Q-net with V-net, a smaller and denser state space makes data more concentrated, contributing to more accurate value estimation and 2) introduce state sparsity to the training by design state-aware-sparsity Markov decision process (MDP), further lessening the impact of sparse states. We theoretically prove the convergence of IEEDS under state-aware-sparsity MDP. Extensive experiments on offline RL benchmarks reveal that IEEDS's superior performance.

Binary Spiking Neural Networks (BSNNs) inherit the event-driven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques. These distinct advantages grant BSNNs lightweight and energy-efficient characteristics, rendering them ideal for deployment on resource-constrained edge devices. However, due to the binary synaptic weights and non-differentiable spike function, effectively training BSNNs remains an open question. In this paper, we conduct an in-depth analysis of the challenge for BSNN learning, namely the frequent weight sign flipping problem. To mitigate this issue, we propose an Adaptive Gradient Modulation Mechanism (AGMM), which is designed to reduce the frequency of weight sign flipping by adaptively adjusting the gradients during the learning process. The proposed AGMM can enable BSNNs to achieve faster convergence speed and higher accuracy, effectively narrowing the gap between BSNNs and their full-precision equivalents. We validate AGMM on both static and neuromorphic datasets, and results indicate that it achieves state-of-the-art results among BSNNs. This work substantially reduces storage demands and enhances SNNs' inherent energy efficiency, making them highly feasible for resource-constrained environments.

Video Moment Retrieval (VMR) involves locating specific moments within a video based on natural language queries. However, existing VMR methods that employ various strategies for cross-modal alignment still face challenges such as limited understanding of fine-grained semantics, semantic overlap, and sparse constraints. To address these limitations, we propose a novel Concept Decomposition Transformer (CDTR) model for VMR. CDTR introduces a semantic concept decomposition module that disentangles video moments and sentence queries into concept representations, reflecting the relevance between various concepts and capturing fine-grained semantics which is crucial for cross-modal matching. These decomposed concept representations are then used as pseudo-labels, determined as positive or negative samples by adaptive concept-specific thresholds. Subsequently, fine-grained concept alignment is performed in video intra-modal and textual-visual cross-modal, aligning different conceptual components within features, enhancing the model's ability to distinguish fine-grained semantics, and alleviating issues related to semantic overlap and sparse constraints. Comprehensive experiments demonstrate the effectiveness of the CDTR, outperforming state-of-the-art methods on three widely used datasets: QVHighlights, Charades-STA, and TACoS.

Event cameras encode visual information by generating asynchronous and sparse event streams, which hold great potential for low latency and low power consumption. Despite many successful implementations of event camera-based applications, most of them accumulate the events into frames and then utilize conventional frame-based computer vision algorithms. These frame-based methods, though typically effective, diminish the inherent advantages of the event camera's low latency and low power consumption. To solve the above problems, we propose ASGCN, which efficiently processes data on an event-by-event basis and dynamically evolves into a corresponding dynamic representation, enabling low latency and high sparsity of data representation. The sparsity computation is further improved by introducing brain-inspired spiking neural networks, resulting in low power consumption for ASGCN. Extensive and diverse experiments demonstrate the energy efficiency and low latency advantages of our processing pipeline. Especially on real-world event camera datasets, our pipeline consumes more than 10,000 times less energy and achieves similar performance compared to current frame-based methods.

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level structure controls, e.g., edge and depth maps, to manipulate the generation process together with text prompts to obtain desired images. This controlling process is globally operated on the entire image, which limits the flexibility of control regions. In this paper, we explore a novel and practical task setting: local control. It focuses on controlling specific local region according to user-defined image conditions, while the remaining regions are only conditioned by the original text prompt. However, it is non-trivial to achieve it. The naive manner of directly adding local conditions may lead to the local control dominance problem, which forces the model to focus on the controlled region and neglect object generation in other regions. To mitigate this problem, we propose Regional Discriminate Loss to update the noised latents, aiming at enhanced object generation in non-control regions. Furthermore, the proposed Focused Token Response suppresses weaker attention scores which lack the strongest response to enhance object distinction and reduce duplication. Lastly, we adopt Feature Mask Constraint to reduce quality degradation in images caused by information differences across the local control region. All proposed strategies are operated at the inference stage. Extensive experiments demonstrate that our method can synthesize high-quality images aligned with the text prompt under local control conditions.

Functional variability in both gray matter (GM) and white matter (WM) is closely associated with human brain cognitive and developmental processes, and is commonly assessed using functional connectivity (FC). However, as a correlationbased approach, FC captures the co-fluctuation between brain regions rather than the intensity of neural activity in each region. Consequently, FC provides only a partial view of functional variability, and this limitation is particularly pronounced in WM, where functional signals are weaker and more susceptible to noise. To tackle this limitation, we introduce fractional amplitude of low-frequency fluctuation (fALFF) to measure the intensity of spontaneous neural activity and analyze functional variability in WM. Specifically, we propose a novel method to quantify WM functional variability by estimating the differential identifiability of fALFF. Higher differential identifiability is observed in WM fALFF compared to FC, which indicates that fALFF is more sensitive to WM functional variability. Through fALFF differential identifiability, we evaluate the functional variabilities of both WM and GM, and find the overall functional variability pattern is similar although WM shows slightly lower variability than GM. The regional functional variabilities of WM are associated with structural connectivity, where commissural fiber regions generally exhibit higher variability than projection fiber regions. Furthermore, we discover that WM functional variability demonstrates a spatial gradient ascending from the brainstem to the cortex by hypothesis testing, which aligns well with the evolutionary expansion. The gradient of functional variability in WM provides novel insights for understanding WM function. To the best of our knowledge, this is the first attempt to investigate WM functional variability via fALFF. Our code is available at https://github.com/Xinle-Chang/WM-fALFF-Idiff-Gradient.

Brain decoding is a key neuroscience field that reconstructs the visual stimuli from brain activity with fMRI, which helps illuminate how the brain represents the world. fMRI-to-image reconstruction has achieved impressive progress by leveraging diffusion models. However, brain signals infused with prior knowledge and associations exhibit a significant information asymmetry when compared to raw visual features, still posing challenges for decoding fMRI representations under the supervision of images. Consequently, the reconstructed images often lack fine-grained visual fidelity, such as missing attributes and distorted spatial relationships. To tackle this challenge, we propose BrainCognizer, a novel brain decoding model inspired by human visual cognition, which explores multilevel semantics and correlations without fine-tuning of generative models. Specifically, BrainCognizer introduces two modules: the Cognitive Integration Module which incorporates prior human knowledge to extract hierarchical region semantics; and the Cognitive Correlation Module which captures contextual semantic relationships across regions. Incorporating these two modules enhances intra-region semantic consistency and maintains interregion contextual associations, thereby facilitating fine-grained brain decoding. Moreover, we quantitatively interpret our components from a neuroscience perspective and analyze the associations between different visual patterns and brain functions. Extensive quantitative and qualitative experiments demonstrate that BrainCognizer outperforms state-of-the-art approaches on multiple evaluation metrics. Our code is released publicly at https://github.com/Grace160/BrainCognizer.

Recent breakthroughs in generative Artificial Intelligence (AI) have ignited a revolutionary wave across information retrieval and recommender systems. This workshop serves as a premier interdisciplinary platform to explore how generative models, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), are transforming multimodal search and recommendation paradigms [3, 6, 9, 10, 12-14]. We aim to convene researchers and practitioners to discuss innovative architectures, methodologies, and evaluation strategies spanning generative document retrieval [5, 8] generative image retrieval [ 7, 16], grounded answer generation [17], generative recommendation [2, 4, 11], and related tasks involving multiple modalities [1,15]. The workshop will facilitate discussions on improving algorithms, generating personalized content, evolving user-system interactions, enhancing trustworthiness, and refining evaluation methodologies for these cutting-edge systems. This timely workshop seeks to identify promising research directions, address key challenges, and foster collaborations towards the development of next-generation intelligent systems.

Quantized Spiking Neural Networks (QSNNs) offer superior energy efficiency and are well-suited for deployment on resource-limited edge devices. However, limited bit-width weight and membrane potential result in a notable performance decline. In this study, we first identify a new underlying cause for this decline: the loss of historical information due to the quantized membrane potential. To tackle this issue, we introduce a memory-free quantization method that captures all historical information without directly storing membrane potentials, resulting in better performance with less memory requirements. To further improve the computational efficiency, we propose a parallel training and asynchronous inference framework that greatly increases training speed and energy efficiency. We combine the proposed memory-free quantization and parallel computation methods to develop a high-performance and efficient QSNN, named MFP-QSNN. Extensive experiments show that our MFP-QSNN achieves state-of-the-art performance on various static and neuromorphic image datasets, requiring less memory and faster training speeds. The efficiency and efficacy of the MFP-QSNN highlight its potential for energy-efficient neuromorphic computing.

A prevalent approach in Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViT) involves freezing the majority of the backbone parameters and solely learning low-rank adaptation weight matrices to accommodate downstream tasks. These low-rank matrices are commonly derived through the multiplication structure of down-projection and up-projection matrices, exemplified by methods such as LoRA and Adapter. In this work, we observe an approximate orthogonality among any two row or column vectors within any weight matrix of the backbone parameters; however, this property is absent in the vectors of the down/up-projection matrices. Approximate orthogonality implies a reduction in the upper bound of the model's generalization error, signifying that the model possesses enhanced generalization capability. If the fine-tuned down/up-projection matrices were to exhibit this same property as the pre-trained backbone matrices, could the generalization capability of fine-tuned ViTs be further augmented? To address this question, we propose an Approximately Orthogonal Fine-Tuning (AOFT) strategy for representing the low-rank weight matrices. This strategy employs a single learnable vector to generate a set of approximately orthogonal vectors, which form the down/up-projection matrices, thereby aligning the properties of these matrices with those of the backbone. Extensive experimental results demonstrate that our method achieves competitive performance across a range of downstream image classification tasks, confirming the efficacy of the enhanced generalization capability embedded in the down/up-projection matrices.

Audio-visual segmentation (AVS) aims to segment objects in videos based on audio cues. Existing AVS methods are primarily designed to enhance interaction efficiency but pay limited attention to modality representation discrepancies and imbalances. To overcome this, we propose the implicit counterfactual framework (ICF) to achieve unbiased cross-modal understanding. Due to the lack of semantics, heterogeneous representations may lead to erroneous matches, especially in complex scenes with ambiguous visual content or interference from multiple audio sources. We introduce the multi-granularity implicit text (MIT) involving video-, segment- and frame-level as the bridge to establish the modality-shared space, reducing modality gaps and providing prior guidance. Visual content carries more information and typically dominates, thereby marginalizing audio features in the decision-making. To mitigate knowledge preference, we propose the semantic counterfactual (SC) to learn orthogonal representations in the latent space, generating diverse counterfactual samples, thus avoiding biases introduced by complex functional designs and explicit modifications of text structures or attributes. We further formulate the collaborative distribution-aware contrastive learning (CDCL), incorporating factual-counterfactual and inter-modality contrasts to align representations, promoting cohesion and decoupling. Extensive experiments on three public datasets validate that the proposed method achieves state-of-the-art performance.

Spiking neural networks (SNNs) are emerging as a promising energy-efficient alternative to traditional artificial neural networks (ANNs) due to their spike-driven paradigm. However, recent research in the SNN domain has mainly focused on enhancing accuracy by designing large-scale Transformer structures, which typically rely on substantial computational resources, limiting their deployment on resource-constrained devices. To overcome this challenge, we propose a quantized spike-driven Transformer baseline (QSD-Transformer), which achieves reduced resource demands by utilizing a low bit-width parameter. Regrettably, the QSD-Transformer often suffers from severe performance degradation. In this paper, we first conduct empirical analysis and find that the bimodal distribution of quantized spike-driven self-attention (Q-SDSA) leads to spike information distortion (SID) during quantization, causing significant performance degradation. To mitigate this issue, we take inspiration from mutual information entropy and propose a bi-level optimization strategy to rectify the information distribution in Q-SDSA. Specifically, at the lower level, we introduce an information-enhanced LIF to rectify the information distribution in Q-SDSA. At the upper level, we propose a fine-grained distillation scheme for the QSD-Transformer to align the distribution in Q-SDSA with that in the counterpart ANN. By integrating the bi-level optimization strategy, the QSD-Transformer can attain enhanced energy efficiency without sacrificing its high-performance advantage. We validate the QSD-Transformer on various visual tasks, and experimental results indicate that our method achieves state-of-the-art results in the SNN domain. For instance, when compared to the prior SNN benchmark on ImageNet, the QSD-Transformer achieves 80.3\% top-1 accuracy, accompanied by significant reductions of 6.0$\times$ and 8.1$\times$ in power consumption and model size, respectively. Code is available at https://github.com/bollossom/QSD-Transformer.

The combination of Spiking Neural Networks (SNNs) and Vision Transformers (ViTs) holds potential for achieving both energy efficiency and high performance, particularly suitable for edge vision applications. However, a significant performance gap still exists between SNN-based ViTs and their ANN counterparts. Here, we first analyze why SNN-based ViTs suffer from limited performance and identify a mismatch between the vanilla self-attention mechanism and spatio-temporal spike trains. This mismatch results in degraded spatial relevance and limited temporal interactions. To address these issues, we draw inspiration from biological saccadic attention mechanisms and introduce an innovative Saccadic Spike Self-Attention (SSSA) method. Specifically, in the spatial domain, SSSA employs a novel spike distribution-based method to effectively assess the relevance between Query and Key pairs in SNN-based ViTs. Temporally, SSSA employs a saccadic interaction module that dynamically focuses on selected visual areas at each timestep and significantly enhances whole scene understanding through temporal interactions. Building on the SSSA mechanism, we develop a SNN-based Vision Transformer (SNN-ViT). Extensive experiments across various visual tasks demonstrate that SNN-ViT achieves state-of-the-art performance with linear computational complexity. The effectiveness and efficiency of the SNN-ViT highlight its potential for power-critical edge vision applications.

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient paradigm for machine intelligence. However, the current SNN community focuses primarily on performance improvement by developing large-scale models, which limits the applicability of SNNs in resource-limited edge devices. In this paper, we propose a hardware-friendly and lightweight SNN, aimed at effectively deploying high-performance SNN in resource-limited scenarios. Specifically, we first develop a baseline model that integrates uniform quantization and structured pruning, called QP-SNN baseline. While this baseline significantly reduces storage demands and computational costs, it suffers from performance decline. To address this, we conduct an in-depth analysis of the challenges in quantization and pruning that lead to performance degradation and propose solutions to enhance the baseline's performance. For weight quantization, we propose a weight rescaling strategy that utilizes bit width more effectively to enhance the model's representation capability. For structured pruning, we propose a novel pruning criterion using the singular value of spatiotemporal spike activities to enable more accurate removal of redundant kernels. Extensive experiments demonstrate that integrating two proposed methods into the baseline allows QP-SNN to achieve state-of-the-art performance and efficiency, underscoring its potential for enhancing SNN deployment in edge intelligence computing.

In-context learning (ICL) has revolutionized natural language processing by enabling models to adapt to diverse tasks with only a few illustrative examples. However, the exploration of ICL within the field of computer vision remains limited. Inspired by Chain-of-Thought (CoT) prompting in the language domain, we propose Chain-of-Focus (CoF) Prompting, which enhances vision models by enabling step-by-step visual comprehension. CoF Prompting addresses the challenges of absent logical structure in visual data by generating intermediate reasoning steps through visual saliency. Moreover, it provides a solution for creating tailored prompts from visual inputs by selecting contextually informative prompts based on query similarity and target richness. The significance of CoF prompting is demonstrated by the recent introduction of Large Autoregressive Vision Models (LAVMs), which predict downstream targets via in-context learning with pure visual inputs. By integrating intermediate reasoning steps into visual prompts and effectively selecting the informative ones, the LAVMs are capable of generating significantly better inferences. Extensive experiments on downstream visual understanding tasks validate the effectiveness of our proposed method for visual in-context learning.

Multi-view clustering has gained significant attention for integrating multi-view information in multimedia applications. With the growing complexity of graph data, multi-view graph clustering (MVGC) has become increasingly important. Existing methods primarily use Graph Neural Networks (GNNs) to encode structural and feature information, but applying GNNs within contrastive learning poses specific challenges, such as integrating graph data with node features and handling both homophilic and heterophilic graphs. To address these challenges, this paper introduces Node-Guided Contrastive Encoding (NGCE), a novel MVGC approach that leverages node features to guide embedding generation. NGCE enhances compatibility with GNN filtering, effectively integrates homophilic and heterophilic information, and strengthens contrastive learning across views. Extensive experiments demonstrate its robust performance on six homophilic and heterophilic multi-view benchmark datasets.

Recently, federated multi-view clustering (FedMVC) has gained attention for its ability to mine complementary clustering structures from multiple clients without exposing private data. Existing methods mainly focus on addressing the feature heterogeneity problem brought by views on different clients and mitigating it using shared client information. Although these methods have achieved performance improvements, the information they choose to share, such as model parameters or intermediate outputs, inevitably raises privacy concerns. In this paper, we propose an Effective and Secure Federated Multi-view Clustering method, ESFMC, to alleviate the dilemma between privacy protection and performance improvement. This method leverages the information-theoretic perspective to split the features extracted locally by clients, retaining sensitive information locally and only sharing features that are highly relevant to the task. This can be viewed as a form of privacy-preserving information sharing, reducing privacy risks for clients while ensuring that the server can mine high-quality global clustering structures. Theoretical analysis and extensive experiments demonstrate that the proposed method more effectively mitigates the trade-off between privacy protection and performance improvement compared to state-of-the-art methods.

Binary Spiking Neural Networks (BSNNs) offer promising efficiency advantages for resource-constrained computing. However, their training algorithms often require substantial memory overhead due to latent weights storage and temporal processing requirements. To address this issue, we propose Binary Spiking Online (BSO) optimization algorithm, a novel online training algorithm that significantly reduces training memory. BSO directly updates weights through flip signals under the online training framework. These signals are triggered when the product of gradient momentum and weights exceeds a threshold, eliminating the need for latent weights during training. To enhance performance, we propose T-BSO, a temporal-aware variant that leverages the inherent temporal dynamics of BSNNs by capturing gradient information across time steps for adaptive threshold adjustment. Theoretical analysis establishes convergence guarantees for both BSO and T-BSO, with formal regret bounds characterizing their convergence rates. Extensive experiments demonstrate that both BSO and T-BSO achieve superior optimization performance compared to existing training methods for BSNNs. The codes are available at https://github.com/hamingsi/BSO.

Transformer-based Spiking Neural Networks (SNNs) introduce a novel event-driven self-attention paradigm that combines the high performance of Transformers with the energy efficiency of SNNs. However, the larger model size and increased computational demands of the Transformer structure limit their practicality in resource-constrained scenarios. In this paper, we integrate binarization techniques into Transformer-based SNNs and propose the Binary Event-Driven Spiking Transformer, i.e. BESTformer. The proposed BESTformer can significantly reduce storage and computational demands by representing weights and attention maps with a mere 1-bit. However, BESTformer suffers from a severe performance drop from its full-precision counterpart due to the limited representation capability of binarization. To address this issue, we propose a Coupled Information Enhancement (CIE) method, which consists of a reversible framework and information enhancement distillation. By maximizing the mutual information between the binary model and its full-precision counterpart, the CIE method effectively mitigates the performance degradation of the BESTformer. Extensive experiments on static and neuromorphic datasets demonstrate that our method achieves superior performance to other binary SNNs, showcasing its potential as a compact yet high-performance model for resource-limited edge devices. The repository of this paper is available at https://github.com/CaoHLin/BESTFormer.

Generating high-fidelity talking heads that maintain stable head poses and achieve robust lip sync remains a significant challenge. Although methods based on 3D Gaussian Splatting (3DGS) offer a promising solution via point-based deformation, they suffer from inconsistent head dynamics and mismatched mouth movements due to unstable Gaussian initialization and incomplete speech features. To overcome these limitations, we introduce SyncGaussian, a 3DGS-based framework that ensures stable head poses, enhanced lip sync, and realistic appearances with real-time rendering. SyncGaussian employs a stable head Gaussian initialization strategy to mitigate head jitter by optimizing commonly used rough head pose parameters. To enhance lip sync, we propose a sync-enhanced encoder that leverages audio-to-text and audio-to-visual speech features. Guided by a tailored cosine similarity loss function, the encoder integrates discriminative speech features through a multi-level sync adaptation mechanism, enabling the learning of an adaptive speech feature space. Extensive experiments demonstrate that SyncGaussian outperforms state-of-the-art methods in image quality, dynamic motion, and lip sync, with the potential for real-time applications.

This paper introduces a soft active surface gripper designed to manipulate fragile objects safely. This gripper consists of two fingers, each equipped with two compliant pneumatic actuators and a soft active surface. The gripper utilizes the elastic belt as its soft active surface, which is driven by a motor, and the opening angle of the elastic band is controlled by pneumatic actuators. The novel design allows for the passive deformation of both the soft active surface and the compliant pneumatic actuator, enabling adaptation to various object shapes and demonstrating superior handling capabilities for delicate items. By synchronizing the opening and closing of the pneumatic fingers with the conveying motion of the active surface, the active surface gripper realizes three degrees of freedom (DOF) for in-plane manipulation, specifically two translational movements and one rotational movement. A prototype gripper has been designed and fabricated for in-plane manipulation experiments with fragile objects, including strawberries, miniature cupcakes, and pears. Experimental results demonstrate that the gripper can execute in-plane in-hand manipulation of fragile objects with varying geometries and dimensions while maintaining secure and robust handling, preventing object slippage and preserving surface integrity without causing damage.

Spiking Neural Networks (SNNs) have garnered significant attention due to their biological plausibility and low power consumption. While spiking transformers enhance performance by combining SNNs with transformer architecture, most rely on rate coding, limiting energy efficiency. Temporal coding methods, such as Time-To-First-Spike (TTFS) coding, offer a more efficient alternative by encoding information based on the timing of a single spike. However, integrating TTFS with transformer architecture faces challenges due to incompatibility with batch normalization (BN) and residual connections (RC), which disrupt the precise spike firing times. In this paper, we propose temporal-coded BN (tBN) and temporal-coded RC (tRC) to address these issues. Building on tBN and tRC, we develop temporal-coded spiking attention (TSA) and temporal-coded spiking transformer (T-SpikeFormer), the first to combine TTFS coding with transformer architecture. Experimental results show our model achieves state-of-the-art performance for temporal-coded SNNs and comparable results to rate-coded SNNs while significantly reducing power consumption.

Augmented Reality (AR) enhances Human-Robot Interaction (HRI) by offering diverse interaction methods. However, existing systems often fail to resolve the conflict between a user's implicit preferences and physical ergonomics, leading to suboptimal experiences. We introduce InteractGuide, a novel framework that, for the first time, uses a Large Language Model (LLM) as a central reasoning engine to dynamically balance these competing factors. Our system translates physiological signals into a symbolic ''Preference Memory'' that the LLM reasons over, alongside real-time ergonomic and contextual data, to provide personalized interaction recommendations. A 29-participant study confirms our architecture improves efficiency and experience compared to single-factor approaches, showing the potential of LLMs as reasoning engines for complex AR-HRI. This work presents a validated end-to-end architecture for user-centric interaction adaptation, demonstrating the potential of LLMs as reasoning engines in complex AR-HRI systems.

In this work, we address the task of table image to LaTeX code generation, with the goal of automating the reconstruction of high-quality, publication-ready tables from visual inputs. A central challenge of this task lies in accurately handling complex tables—those with large sizes, deeply nested structures, and semantically rich or irregular cell content—where existing methods often fail. We begin with a comprehensive analysis, identifying key challenges and highlighting the limitations of current evaluation protocols. To overcome these issues, we propose a reinforced multimodal large language model (MLLM) framework, where a pre-trained MLLM is fine-tuned on a large-scale table-to-LaTeX dataset. To further improve generation quality, we introduce a dual-reward reinforcement learning strategy based on Group Relative Policy Optimization (GRPO). Unlike standard approaches that optimize purely over text outputs, our method incorporates both a structure-level reward on LaTeX code and a visual fidelity reward computed from rendered outputs, enabling direct optimization of the visual output quality. We adopt a hybrid evaluation protocol combining TEDS-Structure and CW-SSIM, and show that our method achieves state-of-the-art performance, particularly on structurally complex tables, demonstrating the effectiveness and robustness of our approach.

Harnessing the event-driven characteristic, Spiking Neural Networks (SNNs) present a promising avenue toward energy-efficient Transformer architectures. However, existing Spiking Transformers still suffer significant performance gaps compared to their Artificial Neural Network counterparts. Through comprehensive analysis, we attribute this gap to these two factors. First, the binary nature of spike trains limits Spiking Self-attention (SSA)’s capacity to capture negative–negative and positive–negative membrane potential interactions on Querys and Keys. Second, SSA typically omits Softmax functions to avoid energy-intensive multiply-accumulate operations, thereby failing to maintain row-stochasticity constraints on attention scores. To address these issues, we propose a Bipolar Self-attention (BSA) paradigm, effectively modeling multi-polar membrane potential interactions with a fully spike-driven characteristic. Specifically, we demonstrate that ternary matrix multiplication provides a closer approximation to real-valued computation on both distribution and local correlation, enabling clear differentiation between homopolar and heteropolar interactions. Moreover, we propose a shift-based Softmax approximation named Shiftmax, which efficiently achieves low-entropy activation and partly maintains row-stochasticity without non-linear operation, enabling precise attention allocation. Extensive experiments show that BSA achieves substantial performance improvements across various tasks, including image classification, semantic segmentation, and event-based tracking. These results establish its potential as a fundamental building block for energy-efficient Spiking Transformers.

Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence, but their continued scaling poses challenges for resource-limited deployment. Despite recent advances in binary SNNs, the storage and computational demands remain substantial for large-scale networks. To further explore the compression and acceleration potential of SNNs, we propose Sub-bit Spiking Neural Networks (S$^2$NNs) that represent weights with less than one bit. Specifically, we first establish an S$^2$NN baseline by leveraging the clustering patterns of kernels in well-trained binary SNNs. This baseline is highly efficient but suffers from \textit{outlier-induced codeword selection bias} during training. To mitigate this issue, we propose an \textit{outlier-aware sub-bit weight quantization} (OS-Quant) method, which optimizes codeword selection by identifying and adaptively scaling outliers. Furthermore, we propose a \textit{membrane potential-based feature distillation} (MPFD) method, improving the performance of highly compressed S$^2$NN via more precise guidance from a teacher model. Extensive results on vision reveal that S$^2$NN outperforms existing quantized SNNs in both performance and efficiency, making it promising for edge computing applications.

Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computational models have greatly advanced, SNNs struggle to achieve competitive performance in visual long-sequence modeling tasks. In artificial neural networks, the effective receptive field (ERF) serves as a valuable tool for analyzing feature extraction capabilities in visual long-sequence modeling. Inspired by this, we introduce the Spatio-Temporal Effective Receptive Field (ST-ERF) to analyze the ERF distributions across various Transformer-based SNNs. Based on the proposed ST-ERF, we reveal that these models suffer from establishing a robust global ST-ERF, thereby limiting their visual feature modeling capabilities. To overcome this issue, we propose two novel channel-mixer architectures: \underline{m}ulti-\underline{l}ayer-\underline{p}erceptron-based m\underline{ixer} (MLPixer) and \underline{s}plash-and-\underline{r}econstruct \underline{b}lock (SRB). These architectures enhance global spatial ERF through all timesteps in early network stages of Transformer-based SNNs, improving performance on challenging visual long-sequence modeling tasks. Extensive experiments conducted on the Meta-SDT variants and across object detection and semantic segmentation tasks further validate the effectiveness of our proposed method. Beyond these specific applications, we believe the proposed ST-ERF framework can provide valuable insights for designing and optimizing SNN architectures across a broader range of tasks. The code is available at \href{https://github.com/EricZhang1412/Spatial-temporal-ERF}{\faGithub~EricZhang1412/Spatial-temporal-ERF}.

The explosive growth in sequence length has intensified the demand for effective and efficient long sequence modeling. Benefiting from intrinsic oscillatory membrane dynamics, Resonate-and-Fire (RF) neurons can efficiently extract frequency components from input signals and encode them into spatiotemporal spike trains, making them well-suited for long sequence modeling. However, RF neurons exhibit limited effective memory capacity and a trade-off between energy efficiency and training speed on complex temporal tasks. Inspired by the dendritic structure of biological neurons, we propose a Dendritic Resonate-and-Fire (D-RF) model, which explicitly incorporates a multi-dendritic and soma architecture. Each dendritic branch encodes specific frequency bands by utilizing the intrinsic oscillatory dynamics of RF neurons, thereby collectively achieving comprehensive frequency representation. Furthermore, we introduce an adaptive threshold mechanism into the soma structure. This mechanism adjusts the firing threshold according to historical spiking activity, thereby reducing redundant spikes while maintaining training efficiency in long-sequence tasks. Extensive experiments demonstrate that our method maintains competitive accuracy while substantially ensuring sparse spikes without compromising computational efficiency during training. These results underscore its potential as an effective and efficient solution for long sequence modeling on edge platforms.

Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typically model the dialogue context as a whole, neglecting the inherent complexity and entanglement within the dialogue. Specifically, a dialogue comprises both focus information and background information, which mutually influence each other. Current methods tend to model these two types of information mixedly, leading to misinterpretation of users' actual needs, thereby lowering the accuracy of recommendations. To address this issue, this paper proposes a novel model to introduce contextual disentanglement for improving conversational recommender systems, named DisenCRS. The proposed model DisenCRS employs a dual disentanglement framework, including self-supervised contrastive disentanglement and counterfactual inference disentanglement, to effectively distinguish focus information and background information from the dialogue context under unsupervised conditions. Moreover, we design an adaptive prompt learning module to automatically select the most suitable prompt based on the specific dialogue context, fully leveraging the power of large language models. Experimental results on two widely used public datasets demonstrate that DisenCRS significantly outperforms existing conversational recommendation models, achieving superior performance on both item recommendation and response generation tasks.

Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by interacting with users through conversations. Most existing studies of CRS focus on extracting user preferences from conversational contexts. However, due to the short and sparse nature of conversational contexts, it is difficult to fully capture user preferences by conversational contexts only. We argue that multi-modal semantic information can enrich user preference expressions from diverse dimensions (e.g., a user preference for a certain movie may stem from its magnificent visual effects and compelling storyline). In this paper, we propose a multi-modal semantic graph prompt learning framework for CRS, named MSCRS. First, we extract textual and image features of items mentioned in the conversational contexts. Second, we capture higher-order semantic associations within different semantic modalities (collaborative, textual, and image) by constructing modality-specific graph structures. Finally, we propose an innovative integration of multi-modal semantic graphs with prompt learning, harnessing the power of large language models to comprehensively explore high-dimensional semantic relationships. Experimental results demonstrate that our proposed method significantly improves accuracy in item recommendation, as well as generates more natural and contextually relevant content in response generation. Code and extended multi-modal CRS datasets are available at https://github.com/BIAOBIAO12138/MSCRS-main.

Conversational Product Search ( CPS ) systems interact with users via natural language to offer personalized and context-aware product lists. However, most existing research on CPS is limited to simulated conversations, due to the lack of a real CPS dataset driven by human-like language. Moreover, existing conversational datasets for e-commerce are constructed for a particular market or a particular language and thus can not support cross-market and multi-lingual usage. In this paper, we propose a CPS data collection protocol and create a new CPS dataset, called PSCon, which assists product search through conversations with human-like language. The dataset is collected by a coached human-human data collection protocol and is available for dual markets and two languages. By formulating the task of CPS, the dataset allows for comprehensive and in-depth research on six subtasks: user intent detection, keyword extraction, system action prediction, question selection, item ranking, and response generation. Moreover, we present a concise analysis of the dataset and propose a benchmark model on the proposed CPS dataset. Our proposed dataset and model will be helpful for facilitating future research on CPS.

The accumulation of adversarial perturbations in the feature space makes it impossible for Deep Neural Networks (DNNs) to know what features are robust and reliable, and thus DNNs can be fooled by relying on a single contaminated feature. Numerous defense strategies attempt to improve their robustness by denoising, deactivating, or recalibrating non-robust features. Despite their effectiveness, we still argue that these methods are under-explored in terms of determining how trustworthy the features are. To address this issue, we propose a novel Evidence-based Multi-Feature Fusion (termed EMFF) for adversarial robustness. Specifically, our EMFF approach introduces evidential deep learning to help DNNs quantify the belief mass and uncertainty of the contaminated features. Subsequently, a novel multi-feature evidential fusion mechanism based on Dempster's rule is proposed to fuse the trusted features of multiple blocks within an architecture, which further helps DNNs avoid the induction of a single manipulated feature and thus improve their robustness. Comprehensive experiments confirm that compared with existing defense techniques, our novel EMFF method has obvious advantages and effectiveness in both scenarios of white-box and black-box attacks, and also prove that by integrating into several adversarial training strategies, we can improve the robustness of across distinct architectures, including traditional CNNs and recent vision Transformers with a few extra parameters and almost the same cost.

Referring Expression Comprehension (REC) is a foundational cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding. It serves as an essential testing ground for Multimodal Large Language Models (MLLMs). To advance this field, we introduced a new REC dataset in our previous conference paper, characterized by two key features. First, it is designed with controllable difficulty levels, requiring multi-level fine-grained reasoning across object categories, attributes, and multi-hop relationships. Second, it incorporates negative text and images generated through fine-grained editing and augmentation, explicitly testing a model's ability to reject scenarios where the target object is absent-an often-overlooked yet critical challenge in existing datasets. In this extended work, we propose two new methods to tackle the challenges of fine-grained REC by combining the strengths of Specialist Models and MLLMs. The first method adaptively assigns simple cases to faster, lightweight models and reserves complex ones for powerful MLLMs, balancing accuracy and efficiency. The second method lets a specialist generate a set of possible object regions, and the MLLM selects the most plausible one using its reasoning ability. These collaborative strategies lead to significant improvements on our dataset and other challenging benchmarks. Our results show that combining specialized and general-purpose models offers a practical path toward solving complex real-world vision-language tasks.

Point clouds serve as the foundational representation of 3D objects, playing a pivotal role in both computer vision and computer graphics. Recently, the acquisition of point clouds has been effortless because of the development of hardware devices. However, the collected point clouds may be incomplete due to environmental conditions, such as occlusion. Therefore, completing partial point clouds becomes an essential task. The majority of current methods address point cloud completion via the utilization of shape priors. While these methods have demonstrated commendable performance, they often encounter challenges in preserving the global structural and geometric details of the 3D shape. In contrast to those mentioned earlier, we propose a novel cross-modal coarse-to-fine network (CMNet) for point cloud completion. Our method utilizes additional image information to provide global information, thus avoiding the loss of structure. To ensure that the generated results contain sufficient geometric details, we propose a coarse-to-fine learning approach based on multiple patches. Specifically, we encode the image and use multiple generators to generate multiple coarse patches, which are combined into a complete shape. Subsequently, based on the coarse patches generated in advance, we generate fine patches by combining partial point cloud information. Experimental results show that our method achieves state-of-the-art performance on point cloud completion.

Action Quality Assessment (AQA) is a challenging task involving analyzing fine-grained technical subactions, aligning high-level visual-semantic representations, and exploring internal temporal structures that capture the overall meaning of given action sequences. To address these challenges, we propose a Visual-semantic Alignment Temporal Parsing Network (VATP-Net) to understand the high-level visual semantics of subaction sequences and internal temporal structures without explicit supervision for action quality assessment. The proposed approach designs a self-supervised temporal parsing module to generate subaction sequences from the given video by aligning the visual and semantic action features. It captures high-level semantics and the internal temporal dynamics of subaction sequences. Furthermore, a multimodal interaction module is proposed to capture the interaction between different modalities of action features, enabling a comprehensive assessment of fine-grained and scene-invariant action details. The proposed module captures the intricate relationships and encourages interactions between different modalities within an action sequence, enhancing the overall understanding of action assessment. We exhaustively evaluate our proposed approach on the MTL-AQA, Rhythmic Gymnastics (RG), FineFS, and Fis-V datasets. Extensive experimental results demonstrate the effectiveness and feasibility of our proposed approach, which outperforms state-of-the-art methods by a significant margin.

Video Moment and Highlight Retrieval (VMHR) aims at retrieving video events with a text query in a long untrimmed video and selecting the most related video highlights by assigning the worthiness scores. However, we observed existing methods mostly have two unavoidable defects: 1) The temporal annotations of highlight scores are extremely labor-cost and subjective, thus it is very hard and expensive to gather qualified annotated training data. 2) The previous VMHR methods would fit the temporal distributions instead of learning vision-language relevance, which reveals the limitations of the conventional paradigm on model robustness towards biased training data from open-world scenarios. In this paper, we propose a novel method termed Query as Supervision (QaS), which jointly tackles the annotation cost and model robustness in the VMHR task. Specifically, instead of learning from the distributions of temporal annotations, our QaS method completely learns multimodal alignments within semantic space via our proposed Hybrid Ranking Learning scheme for retrieving moments and highlights. In this way, it only requires low-cost annotations and also provides much better robustness towards Out-Of-Distribution test samples. We evaluate our proposed QaS method on three benchmark datasets, i.e., QVHighlights, BLiSS, and Charades-STA and their biased training version. Extensive experiments demonstrate that the QaS outperforms existing state-of-the-art methods under the same low-cost annotation settings and reveals better robustness against biased training data. Our code is available athttps://github.com/CFM-MSG/Code_QaS.

Text-based person retrieval (TBPR) is a challenging task that aims at retrieving candidate pedestrian images from a gallery, using textual descriptions as queries. Existing methods generally assume that the textual query and the unique candidate image have a certain cross-modal relationship under one-to-one constraint, and optimize their conditional probability via a discriminative paradigm. However, in real scenarios of TBPR, a textual query may associate with multiple candidate images at one time, indicating that the uncertainty resides in the one-to-many cross-modal relationship. Moreover, the learnt conditional probabilities from the discriminative paradigm of existing methods may be less effective in reflecting the joint probabilities of the textual query and candidate images. To tackle these problems, we propose a novel method termed Cross-modal Uncertainty Modeling with Diffusion-based Refinement (CUMDR) for the TBPR task. First, we implicitly model the cross-modal uncertainty to capture richer semantics and complex correlations, thus generating diverse yet plausible retrievals. Additionally, to reasonably mitigate the impact of noisy data with high uncertainty, we quantify the uncertainty to allocate the importance of raw and complement annotations, which are generated from the multi-modal large language model based on the retrieval-augmented template. Finally, we propose a novel diffusion-based denoiser to progressively refine cross-modal alignments by learning joint probabilities. Extensive experiments on three TBPR datasets demonstrate the superior performance and generalizability of our CUMDR approach compared to the latest methods. Our anonymous implementation repository is available athttps://github.com/Shenshen7/CUMDR.

Grounding DINO (GDINO) has strong potential for use in zero-shot detection and data annotation, but its use is limited by high computational costs. In addition, YOLOX allows real-time detection but struggles to perform well in complex scenes. To address this challenge, we propose an edge-cloud collaborative framework for an Advanced Driver Assistance System (ADAS) to enhance real-time detector performance on edge devices by leveraging the robust capabilities of cloud-based multimodal detectors to improve perception in complex environments. Our framework consists of cloud and edge components: on the cloud side, we propose a distillation method for multimodal object detectors, which is referred to as MMKD, to optimize the performance of GDINO. Specifically, we use a two-stage distillation strategy, including Cross-modal Listwise Distillation (CLD) and Risk-focused Pseudo-label Distillation (RPLD). With MMKD, we successfully deploy the GDINO model to the cloud, achieving a 1.4% improvement in average precision (AP) and a 1.7× increase in inference speed. On the edge side, leveraging this streamlined version of GDINO, we propose an ADAS data engine to construct a 1.5 Million-scale GDINO-based Dataset for ADAS, named GDDA1.5M. Impressively, on the basis of YOLOX-Lite, we develop a lightweight object detector that is optimized for the application of an ADAS on edge devices through pruning and architectural refinements. Leveraging the GDDA1.5M dataset and the RPLD training strategy, the model achieves a 7.5% improvement in AP, substantially surpassing its counterparts that were trained on 300K manually labeled images. After the YOLOX-Lite detector is deployed on edge devices within our proposed edge-cloud collaborative framework, it achieves an inference speed of 18 milliseconds on the Horizon X3E chip, while the cloud-based distilled model functions efficiently in complex environments.

Supervised dehazing models, trained on synthetic hazy-clean image pairs, often face a notable decline in performance when applied to real-world scenes. Consequently, CycleGAN-based unpaired dehazing methods are proposed to improve the model’s generalization. One successful approach among these methods involves decomposing the physical properties of the atmospheric scattering model (ASM). However, estimating physical properties individually from input images is difficult without supervised labels, which ignores the semantic consistency between different physical regions. We claim semantic region information can offer additional geometric spatial constraints for estimating physical properties, as natural images can be divided into regions with similar scene depths. Motivated by this, we propose a novel generalized and realistic unpaired image dehazing framework via region-aware physical constraints (RPC-Dehaze). Our approach utilizes fine-grained semantic region maps from the Segment Anything Model (SAM) in a specially designed region prompt enhancement module. This enables the dehazing and hazing cyclic networks to learn region-aware physical constraints, leading to accurate estimation of haze imaging physical properties. In contrast to existing unpaired methods that treat dehazing and hazing networks equally, we incorporate Retinex theory into the hazing network, allowing it to learn diverse illumination effects in different regions. We adaptively refine the Retinex-based illumination component, resulting in more realistic hazy images. To further facilitate unsupervised learning in our framework, we propose a physical consensual contrastive regularization to ensure compact representation constraints in the latent feature space. Extensive experiments on synthetic and real image datasets show our method surpasses state-of-the-art unpaired dehazing methods in both effectiveness and generalization capability.

Video grounding tasks have recently gained significant attention. However, existing methods failed to fully comprehend the semantics within queries and videos, often overlooking key content. Moreover, the lack of fine-grained cross-modal alignment and interaction to guide the semantic matching of complex texts and videos lead to inconsistent representational modeling. To address this issue, we propose a Semantic Hierarchical Grounding model, referred to as SHG, and design a cross-modal semantic hierarchical graph to achieve fine-grained semantic understanding. SHG decomposes both the query and each video moment into three levels: global, action, and element. This topology, ranging from global to local, establishes multigranularity intrinsic connections between the two modalities, fostering a comprehensive understanding of dynamic semantics and fine-grained cross-modal matching. Accordingly, to fully leverage the rich information within the cross-modal semantic hierarchical graph, we employ contrastive learning by seeking samples with the same action and element semantics, then achieve node-moment cross-modal hierarchical matching for global alignment. This approach can unearth fine-grained clues and align semantics across multiple granularities. Moreover, we combine the designed hierarchical graph interaction for coarse-to-fine fusion of text and video, thereby enabling highly accurate video grounding. Extensive experiments conducted on three challenging public datasets (ActivityNet-Captions, TACoS, and Charades-STA) demonstrate that the proposed approach outperforms state-of-the-art techniques, validating its effectiveness.

Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies. Recent studies often rely on transformer-based models to address these issues and achieve cross-modal fusion detection. However, the quadratic computational complexity of transformers limits their performance in remote sensing imagery. Inspired by the efficiency and lower complexity of Mamba in long sequence tasks, we propose Disparity-guided Multispectral Mamba (DMM), a multispectral oriented object detection framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task. The DCFM module leverages disparity information between modalities to adaptively merge features from RGB and IR images, mitigating inter-modal conflicts. The MTA module aims to enhance feature representation by focusing on relevant target regions within the RGB modality, addressing intra-modal variations. The TPA auxiliary task utilizes single-modal labels to guide the optimization of the MTA module, ensuring it focuses on targets and their local context. Extensive experiments on the DroneVehicle and VEDAI datasets demonstrate the effectiveness of our method, which outperforms state-of-the-art methods while maintaining computational efficiency. Code will be available at https://github.com/Another-0/DMM.

Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts. Multi-concept customization emerges as the challenging task within this domain. Existing approaches often rely on training a fusion matrix of multiple Low-Rank Adaptations (LoRAs) to merge various concepts into a single image. However, we identify this straightforward method faces two major challenges: 1) concept confusion, where the model struggles to preserve distinct individual characteristics, and 2) concept vanishing, where the model fails to generate the intended subjects. To address these issues, we introduce LoRA-Composer, a training-free framework designed for seamlessly integrating multiple LoRAs, thereby enhancing the harmony among different concepts within generated images. LoRA-Composer addresses concept vanishing through concept injection constraints, enhancing visibility via an expanded cross-attention mechanism. To combat concept confusion, concept isolation constraints are introduced, refining the self-attention computation. Furthermore, we propose two inference techniques to accelerate inference speed without performance degradation and enhance the accuracy of the generated region, respectively. Extensive experiments demonstrate that LoRA-Composer significantly outperforms standard baselines, especially in scenarios without image-based conditions such as canny edge or pose estimation.

Existing underwater salient object detection (USOD) methods design fusion strategies to integrate multimodal information, but lack exploration of modal characteristics. To address this, we separately leverage the RGB and depth branches to learn disentangled representations, formulating the heterogeneous experts and hierarchical perception network (HEHP). Specifically, to reduce modal discrepancies, we propose the hierarchical prototype guided interaction (HPI), which achieves fine-grained alignment guided by the semantic prototypes, and then refines with complementary modalities. We further design the mixture of frequency experts (MoFE), where experts focus on modeling high- and low-frequency respectively, collaborating to explicitly obtain hierarchical representations. To efficiently integrate diverse spatial and frequency information, we formulate the four-way fusion experts (FFE), which dynamically selects optimal experts for fusion while being sensitive to scale and orientation. Since depth maps with poor quality inevitably introduce noises, we design the uncertainty injection (UI) to explore high uncertainty regions by establishing pixel-level probability distributions. We further formulate the holistic prototype contrastive (HPC) loss based on semantics and patches to learn compact and general representations across modalities and images. Finally, we employ varying supervision based on branch distinctions to implicitly construct difference modeling. Extensive experiments on two USOD datasets and four relevant underwater scene benchmarks validate the effect of the proposed method, surpassing state-of-the-art binary detection models. Impressive results on seven natural scene benchmarks further demonstrate the scalability.

Semi-supervised learning methods, compared to fully supervised learning, offer significant potential to alleviate the burden of manual annotations on clinicians. By leveraging unlabeled data, these methods can aid in the development of medical image segmentation systems for improving efficiency. Boundary segmentation is crucial in medical image analysis. However, accurate segmentation of boundary regions is under-explored in existing methods since boundary pixels constitute only a small fraction of the overall image, resulting in suboptimal segmentation performance for boundary regions. In this paper, we introduce boundary-guided contrastive learning for semi-supervised medical image segmentation (BoCLIS). Specifically, we first propose conservative-to-radical teacher networks with an uncertainty-weighted aggregation strategy to generate higher quality pseudo-labels, enabling more efficient utilization of unlabeled data. To further improve the performance of segmentation in boundary regions, we propose a boundary-guided patch sampling strategy to guide the framework in learning discriminative representations for these regions. Lastly, the patch-based contrastive learning is proposed to simultaneously compute the (dis)similarities of the discriminative representations across intra- and inter-images. Extensive experiments on three public datasets show that our method consistently outperforms existing methods, especially in the boundary region, with DSC improvements of 20.47%, 16.75%, and 17.18%, respectively. A comprehensive analysis is further performed to demonstrate the effectiveness of our approach. Our code is released publicly at https://github.com/youngyzzZ/BoCLIS.

With coordinates as the input and RGB pixel values as the output, a neural network can be used to represent an image, which is widely known as Implicit neural representations (INRs). Previous works on INR have mainly focused on learning an invariant image target without exploring the impact of learning strategies on learning INR. It is observed that there is a substantial variation in PSNR among different images, and our preliminary investigation shows that, in the early training stage, learning complex image content yields significantly better performance than simple image content. Inspired by this finding, we conjecture that increasing INR task complexity in the early stage of training might boost INR performance and thus propose to intentionally contaminate the target image with another complex image. Our proposed method is called Mix-INR, which adopts a two-stage training to first learn a pseudo-target image (contaminated target) and then learn the real-target image (uncontaminated target). To generate the pseudo-target image, we experiment with two contamination methods (blending and replacement), both of which show superior performance and verify our conjecture. INRs have gained popularity as a promising approach for representing a variety of data types, including images of the task complexity of the pseudo-target image, we set the contamination image from a complex natural image to a random-noise image. Moreover, we propose a dynamic contamination method to smoothly transition from the pseudo-target image to the real-target image. Experimental results demonstrate that our proposed method achieves competitive performance, which suggests that INR can be improved by manipulating the task complexity in the early stage of training.

Label biases in facial expression recognition (FER) datasets, caused by annotators' subjectivity, pose challenges in improving the performance of target datasets when auxiliary labeled data are used. Moreover, training with multiple datasets can lead to visible degradations in the target dataset. To address these issues, we propose a novel framework called the AU-aware Vision Transformer (AU-ViT), which leverages unified action unit (AU) information and discards expression annotations of auxiliary data. AU-ViT integrates an elaborately designed AU branch in the middle part of a master ViT to enhance representation learning during training. Through qualitative and quantitative analyses, we demonstrate that AU-ViT effectively captures expression regions and is robust to real-world occlusions. Additionally, we observe that AU-ViT also yields performance improvements on the target dataset, even without auxiliary data, by utilizing pseudo AU labels. Our AU-ViT achieves performances superior to, or comparable to, that of the state-of-the-art methods on FERPlus, RAFDB, AffectNet, LSD and the other three occlusion test datasets.

Temporal Sentence Grounding (TSG) requires a thorough understanding of the complex cross-modal semantic relationships between videos and text. However, existing methods fail to accurately capture content at diverse granularity levels with distinct semantics, making it difficult to achieve fine alignment of visuals and text. To overcome this issue, we attempt to mine for rich semantic clues by utilizing the hierarchical correspondence structure and multi-granularity visual-to-text reconstruction, achieving fine-grained reasoning. Specifically, for the TSG task, we propose a novel Hierarchical Cross-modal Fine-grained Mining Network (HCFMN), which utilizes an attention mechanism based on temporal hierarchical relationships to extract temporal features corresponding to the text of different granularities. We leverage the reconstructability of visual-to-text, recovering multi-granularity textual content from coarse to fine by focusing on temporal features at different layers, hierarchically extracting temporal features and the dependencies related to the text, and implementing fine-grained cross-modal semantic alignment. Furthermore, HCFMN introduces a novel partitioned efficient attention mechanism, which significantly enhances the model’s efficiency through a two-stage attention based on sequence and channel compression. Extensive experimental results on three public datasets (ActivityNet-Captions, TACoS, and Charades-STA) demonstrate that the proposed method achieves state-of-the-art performance.

Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant attention. They are extensively utilized for image representation, with pixel coordinates as input and pixel values as output. In contrast to prior works focusing on investigating the effect of the model's inside components (activation function, for instance), this work pioneers the exploration of the effect of kernel transformation of input/output while keeping the model itself unchanged. A byproduct of our findings is a simple yet effective method that combines scale and shift to significantly boost INR with negligible computation overhead. Moreover, we present two perspectives, depth and normalization, to interpret the performance benefits caused by scale and shift transformation. Overall, our work provides a new avenue for future works to understand and improve INR through the lens of kernel transformation.

Despite its significant progress, cross-modal retrieval still suffers from one-to-many matching cases, where the multiplicity of semantic instances in another modality could be acquired by a given query. However, existing approaches usually map heterogeneous data into the learned space as deterministic point vectors. In spite of their remarkable performance in matching the most similar instance, such deterministic point embedding suffers from the insufficient representation of rich semantics in one-to-many correspondence. To address the limitations, we intuitively extend a deterministic point into a closed geometry and develop geometric representation learning methods for cross-modal retrieval. Thus, a set of points inside such a geometry could be semantically related to many candidates, and we could effectively capture the semantic uncertainty. We then introduce two types of geometric matching for one-to-many correspondence, i.e., point-to-rectangle matching (dubbed P2RM) and rectangle-to-rectangle matching (termed R2RM). The former treats all retrieved candidates as rectangles with zero volume (equivalent to points) and the query as a box, while the latter encodes all heterogeneous data into rectangles. Therefore, we could evaluate semantic similarity among heterogeneous data by the Euclidean distance from a point to a rectangle or the volume of intersection between two rectangles. Additionally, both strategies could be easily employed for off-the-self approaches and further improve the retrieval performance of baselines. Under various evaluation metrics, extensive experiments and ablation studies on several commonly used datasets, two for image-text matching and two for video-text retrieval, demonstrate our effectiveness and superiority.

Large language models (LLMs) have gained a lot of attention and achievements recently because of their significant comprehension and generative abilities. However, the large-scale parameters of LLMs require considerable computational resources in the training and inference process, which restricts their wide application. To overcome this challenge, we propose an efficient mixed precision weight quantization (EMWQ) method for LLMs in this article. Specifically, we introduce a new outlier detection method by analyzing the weight distribution instead of the conventional weight magnitude. Then, we propose a dual-quantization strategy that quantizes both the outlier critical columns and the residual matrices with different precision. Besides, we introduce two effective EMWQ-based application frameworks, the EMWQ-R and EMWQ-O in our study. Comprehensive experiments are conducted on the Penn Treebank (PTB), C4, ARC-Easy datasets, and MMLU benchmark across various tasks. The comparison results demonstrate that the proposed EMWQ achieves state-of-the-art performance in mixed precision quantization and further reduces computational memory cost. Besides, it has higher generalizability compared with conventional methods.

The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processes within the brain remains limited. However, no existing artificial intelligence models have yet matched human-level capabilities in terms of memory storage and retrieval. This study introduces a brain-inspired spiking neural model that integrates the learning and forgetting processes of sequential memory. The proposed model closely mimics the distributed and sparse temporal coding observed in the biological neural system. It employs one-shot online learning for memory formation and uses biologically plausible mechanisms of neural oscillation and phase precession to retrieve memorized sequences reliably. In addition, an active forgetting mechanism is integrated into the spiking neural model, enabling memory removal, flexibility, and updating. The proposed memory model not only enhances our understanding of human memory processes but also provides a robust framework for addressing temporal modeling tasks.

Point cloud completion aims at completing shapes from their partial. Most existing methods utilized shape’s priors information for point cloud completion, such as inputting the partial and getting the complete one through an encoder-decoder deep learning structure. However, it is very often to easily cause the loss of information in the generation process because of the invisibility of missing areas. Unlike most existing methods directly inferring the missing points using shape priors, we address it as a cross-modality task. We propose a new Cross-modal Dual Phases Network (CDPNet) for shape completion. Our key idea is that the global information of the shape is obtained from the extra single-view image, and the partial point clouds provide the geometric information. After that, the multi-modal features jointly guide the specific structural information. To learn the geometric details of the shape, we chose to use patches to preserve the local geometric feature. In this way, we can generate shapes with enough geometric details. Experimental results show that our method achieves state-of-the-art performance on point cloud completion.

Text-based person retrieval aims at retrieving a specific pedestrian image from a gallery based on textual descriptions. The primary challenge is how to overcome the inherent heterogeneous modality gap in the situation of significant intra-class variation and minimal inter-class variation. Existing approaches commonly employ vision-language pre-training or attention mechanisms to learn appropriate cross-modal alignments from noise inputs. Despite commendable progress, current methods inevitably suffer from two defects: 1) Matching ambiguity, which mainly derives from unreliable matching pairs; 2) One-sided cross-modal alignments, stemming from the absence of exploring one-to-many correspondence, i.e., coarse-grained semantic alignment. These critical issues significantly deteriorate retrieval performance. To this end, we propose a novel framework termed Adaptive Uncertainty-based Learning (AUL) for text-based person retrieval from the uncertainty perspective. Specifically, our AUL framework consists of three key components: 1) Uncertainty-aware Matching Filtration that leverages Subjective Logic to effectively mitigate the disturbance of unreliable matching pairs and select high-confidence cross-modal matches for training; 2) Uncertainty-based Alignment Refinement, which not only simulates coarse-grained alignments by constructing uncertainty representations but also performs progressive learning to incorporate coarse- and fine-grained alignments properly; 3) Cross-modal Masked Modeling that aims at exploring more comprehensive relations between vision and language. Extensive experiments demonstrate that our AUL method consistently achieves state-of-the-art performance on three benchmark datasets in supervised, weakly supervised, and domain generalization settings. Our code is available at https://github.com/CFM-MSG/Code-AUL.

The sound mixture separation is still challenging due to heavy sound overlapping and disturbance from noise. Unsupervised separation would significantly increase the difficulty. As sound overlapping always hinders accurate sound separation, we propose an Independency Adversarial Learning based Cross-Modal Sound Separation (IAL-CMS) approach, where IAL employs adversarial learning to minimize the correlation of separated sound elements, exploring high sound independence; CMS performs cross-modal sound separation, incorporating audio-visual consistent feature learning and interactive cross-attention learning to emphasize the semantic consistency among cross-modal features. Both audio-visual consistency and audio consistency are kept to guarantee accurate separation. The consistency and sound independence ensure the decomposition of overlapping mixtures into unrelated and distinguishable sound elements. The proposed approach is evaluated on MUSIC, VGGSound, and AudioSet. Extensive experiments certify that our approach outperforms existing approaches in supervised and unsupervised scenarios.

Temporal sentence localization (TSL) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant yet expensive manual annotations for training. Moreover, these trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, in this paper, we target another more practical but challenging setting: unsupervised domain adaptative temporal sentence localization (UDA-TSL), which explores whether the localization knowledge can be transferred from a fully-annotated data domain (source domain) to a new unannotated data domain (target domain). Particularly, we propose an effective and novel baseline for UDA-TSL to bridge the multi-modal gap across different domains and learn the potential correspondence between the video-query pairs in target domain. We first develop separate modality-specific domain adaptation modules to smoothly balance the minimization of the domain shifts in cross-dataset video and query domains. Then, to fully exploit the semantic correspondence of both modalities in target domain for unsupervised localization, we devise a mutual information learning module to adaptively align the video-query pairs which are more likely to be relevant in target domain, leading to more truly aligned target pairs and ensuring the discriminability of target features. In this way, our model can learn domain-invariant and semantic-aligned cross-modal representations. Three sets of migration experiments show that our model achieves competitive performance compared to existing methods.

Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearance. To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding. Specifically, a new task termed Embodied Reference Understanding (ERU) is first designed for this concern. Then a new dataset called ScanERU is constructed to evaluate the effectiveness of this idea. Different from existing datasets, our ScanERU dataset is the first to cover semi-synthetic scene integration with textual, real-world visual, and synthetic gestural information. Additionally, this paper formulates a heuristic framework based on attention mechanisms and human body movements to enlighten the research of ERU. Experimental results demonstrate the superiority of the proposed method, especially in the recognition of multiple identical objects. Our codes and dataset are available in the ScanERU repository.

Mirror detection is of great significance for avoiding false recognition of reflected objects in computer vision tasks. Existing mirror detection frameworks usually follow a supervised setting, which relies heavily on high quality labels and suffers from poor generalization. To resolve this, we instead propose the first weakly-supervised mirror detection framework and also provide the first scribble-based mirror dataset. Specifically, we relabel 10,158 images, most of which have a labeled pixel ratio of less than 0.01 and take only about 8 seconds to label. Considering that the mirror regions usually show great scale variation, and also irregular and occluded, thus leading to issues of incomplete or over detection, we propose a local-global feature enhancement (LGFE) module to fully capture the context and details. Moreover, it is difficult to obtain basic mirror structure using scribble annotation, and the distinction between foreground (mirror) and background (non-mirror) features is not emphasized caused by mirror reflections. Therefore, we propose a foreground-aware mask attention (FAMA), integrating mirror edges and semantic features to complete mirror regions and suppressing the influence of backgrounds. Finally, to improve the robustness of the network, we propose a prototype contrast loss (PCL) to learn more general foreground features across images. Extensive experiments show that our network outperforms relevant state-of-the-art weakly supervised methods, and even some fully supervised methods. The dataset and codes are available at https://github.com/winter-flow/WSMD.

Conversational Product Search (CPS) provides an engaging way for users to find products through effective natural language conversations. However, understanding the effect of conversational characteristics on user search performance and when to ask clarifying questions or recommend products remains unexplored. To fill the gap, we conduct an empirical study in this paper. Specifically, we developed a conversational system that allows participants to join as customers or shopping assistants, to simulate the conversational product search activity. Data collected from conversations and participant feedback indicate that: (a) CPS systems tend to ask clarifying questions early in the conversation when users express the intent of issuing a new query and chitchat, while they tend to recommend products at a later stage of conversations; asking clarifying questions early and recommending products lately can significantly improve search performance and user's satisfaction; (b) asking clarifying questions and more fine-grained search keywords positively influence search performance in terms of finding relevant products; (c) although the conversation time has a positive impact on the number of recommended products, the performance gain diminishes with longer conversation time; (d) more clarifying questions, more conversation turns, and longer system response time lead to decreased user satisfaction.

Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://github.com/zstarN70/RLRR.git.

With the advent of ensemble-based attacks, the transfer-ability of generated adversarial examples is elevated by a noticeable margin despite many methods only employing superficial integration yet ignoring the diversity between ensemble models. However, most of them compromise the latent value of the diversity between generated perturbation from distinct models which we argue is also able to increase the adversarial transferability, especially heterogeneous at-tacks. To address the issues, we propose a novel method of Stochastic Mini-batch black-box attack with Ensemble Reweighing using reinforcement learning (SMER) to produce highly transferable adversarial examples. We emphasize the diversity between surrogate models achieving indi-vidual perturbation iteratively. In order to customize the individual effect between surrogates, ensemble reweighing is introduced to refine ensemble weights by maximizing attack loss based on reinforcement learning which functions on the ultimate transferability elevation. Extensive exper-iments demonstrate our superiority to recent ensemble at-tacks with a significant margin across different black-box attack scenarios, especially on heterogeneous conditions. https://github.com/tangbwb/SMER

Text-based person retrieval (TBPR) involves retrieving pedestrian images from a gallery using textual queries. Existing methods assume that all the training pairs are correct and the textual query only corresponds to one image. However, in practical scenarios of TBPR, there indeed exists data noise and many-to-many matching relationships between semantically similar images and textual queries. To address these problems, we propose a novel approach termed Diverse Embedding Modeling (DEM) with Adaptive Noise Filter for the TBPR task. Firstly, we propose a dynamic margin to measure the degree of noise, which can adaptively reduce the weights of image-text pairs with severe noise during training, thereby effectively mitigating the impact of noisy pairs. Moreover, we model diverse visual and textual embeddings from learnable parameterized distributions, which aim to simulate the many-to-many matching scenarios. Extensive experiments conducted on three TBPR datasets demonstrate the superior performance of our DEM method compared to recent state-of-the-art methods.

Supervised dehazing models trained on synthetic datasets exhibit severe performance degradation in real-world scenarios due to the domain gap. Unsupervised methods are proposed to process real hazy images, while they suffer from the complex training procedure. In this paper, we present a universal domain prompt learning framework (DPLF) for boosting the performance of supervised dehazing models in real scenarios by introducing prompt learning and well-designed domain adapters. We train the learnable text prompts by CLIP feature alignment, which can discriminate between real hazy and clean images, and use these prompts as unsupervised text constraints. Notably, the distribution gap between synthetic and real haze can be regarded as the difference of the haze-relevant style domain. Motivated by this, we design the style domain prompt adapter to align features from synthetic and real haze domains. Extensive experiments on real-world datasets demonstrate significant performance improvement of the baseline dehazing models with our DPLF.

An intuitive strategy for generating more transferable adversarial examples is to absorb the advantages of different surrogates in an ensemble. However, existing ensemble adversarial attacks simply average the outputs of various models while ignoring their different contributions. To quantify the importance of various surrogates, we propose a novel Shapley Ensemble Adversarial Attack (dubbed SEAA) – an effective algorithm that allocates weights based on Shapley values of the surrogates. Specifically, the synthesis of adversarial examples in an ensemble adversarial attack is firstly regarded as a cooperative game process of multiple surrogates from the perspective of contribution. As the Shapley value is known as an effective decision metric in cooperative game theory, it is thus intuitively introduced into this work to accurately evaluate the contribution of a surrogate by reweighing its importance at each iteration, thus avoiding local optimality and steering the generation of adversarial examples. Comprehensive experimental results demonstrate our effectiveness.

The Weakly-Supervised Moment Retrieval and Highlight Detection (WS-MRHD) task aims at retrieving target moments and highlights in an untrimmed video with a semantic relevant text query. One of the most challenging problems in this task is the absence of reliable temporal supervision signals. In this paper, we propose a Temporal Self-paced Proposal Learning (TSPL) method to perform a progressive temporal proposal selection mechanism. It productively improves the effectiveness of contrastive learning even when the frame-level annotations are inaccessible. Specifically, our proposed TSPL method consists of three key components: (1) The Variance-Based Instance Selection (VBIS) module leverages self-paced learning for dynamic temporal proposal selection. (2) A Highlight Broadcasting (HB) module to combine reliable time spans and assign frame-level pseudo labels. (3) A Negative Sample Learning (NSL) module to align the text query with relevant video segments. By dynamically selecting the appropriate temporal proposals for training, our TSPL method conducts more reliable cross-modal alignment thus remarkably boosting retrieval performance. The extensive experiments on two WS-MRHD public benchmarks verify our proposed TSPL method substantially outperforms current state-of-the-art methods.

Cross-modal coherence modeling is essential for intelligent systems to help them organize and structure information, thereby understanding and creating content of the physical world coherently like human-beings. Previous work on cross-modal coherence modeling attempted to leverage the order information from another modality to assist the coherence recovering of the target modality. Despite of the effectiveness, labeled associated coherency information is not always available and might be costly to acquire, making the cross-modal guidance hard to leverage. To tackle this challenge, this paper explores a new way to take advantage of cross-modal guidance without gold labels on coherency, and proposes the Weak Cross-Modal Guided Ordering (WeGO) model. More specifically, it leverages high-confidence predicted pairwise order in one modality as reference information to guide the coherence modeling in another. An iterative learning paradigm is further designed to jointly optimize the coherence modeling in two modalities with selected guidance from each other. The iterative cross-modal boosting also functions in inference to further enhance coherence prediction in each modality. Experimental results on two public datasets have demonstrated that the proposed method outperforms existing methods for cross-modal coherence modeling tasks. Major technical modules have been evaluated effective through ablation studies. Codes are available at: \url{https://github.com/scvready123/IterWeGO}.

Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Understanding artworks is challenging due to its subjective nature, diverse interpretations, and complex visual elements, requiring expertise in art history, cultural background, and aesthetic theory. However, limited by the data collection and model ability, previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. To facilitate the research progress, in this paper, we step further to compose comprehensive analysis inspired by the remarkable perception and generation ability of large multimodal models. Specifically, we first propose a task of composing paragraph analysis for artworks, i.e., painting in this paper, only focusing on visual characteristics to formulate more comprehensive understanding of artworks. To support the research on formal analysis, we collect a large dataset PaintingForm, with about 19k painting images and 50k analysis paragraphs. We further introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture leveraging our collected data. We conduct formal analysis generation and zero-shot experiments across several datasets to assess the capacity of our model. The results show remarkable performance improvements comparing with powerful baseline LMMs, demonstrating its superb ability of art analysis and generalization. \textcolor{blue}{The codes and model are available at: https://github.com/steven640pixel/GalleryGPT.

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event forecasting, i.e., highlighting and complementary. Then, we develop a novel framework, named MM-Forecast. It employs an Image Function Identification module to recognize these functions as verbal descriptions using multimodal large language models (MLLMs), and subsequently incorporates these function descriptions into LLM-based forecasting models. To evaluate our approach, we construct a new multimodal dataset, MidEast-TE-mm, by extending an existing event dataset MidEast-TE-mini with images. Empirical studies demonstrate that our MM-Forecast can correctly identify the image functions, and further more, incorporating these verbal function descriptions significantly improves the forecasting performance. The dataset, code, and prompts are available at https://github.com/LuminosityX/MM-Forecast.

Prior research on emotion recognition in extended reality (XR) has faced challenges due to the occlusion of facial expressions by Head-Mounted Displays (HMDs). This limitation hinders accurate Facial Expression Recognition (FER), which is crucial for immersive user experiences. This study aims to overcome the occlusion challenge by integrating physiological signals with partially visible facial expressions to enhance emotion recognition in XR environments. We employed a multi-task approach, utilizing a feature-level fusion to fuse Electroencephalography (EEG) and Galvanic Skin Response (GSR) signals with occluded facial expressions. The model predicts valence and arousal simultaneously from both macro-and micro-expression. Our method demonstrated improved accuracy in emotion recognition under partial occlusion conditions. The integration of temporal physiological signals with other modalities significantly enhanced performance, particularly for half-face emotion recognition. The study presents a novel approach to emotion recognition in XR, addressing the limitations of facial occlusion by HMDs. The findings suggest that physiological signals are vital for interpreting emotions in occluded scenarios, offering potential for real-time applications and advancing social XR applications.

Previous research has demonstrated the potential of Augmented Reality in enhancing psychological comfort in Human-Robot Interaction (AR-HRI) through shared robot intent, enhanced visual feedback, and increased expressiveness and creativity in interaction methods. However, the challenge of selecting interaction methods that enhance physical comfort in varying scenarios remains. This study purposes a dynamic dual-layer interaction adjustment mechanism to improve user comfort and interaction efficiency. The mechanism comprises two models: an general layer model, grounded in ergonomics principles, identifies appropriate areas for various interaction methods; a individual layer model predicts user discomfort levels using physiological signals. Interaction methods are dynamically adjusted based on discomfort level changes, enabling the system to adapt to individual differences and dynamic changes, thereby reducing misjudgments and enhancing comfort management. The mechanism's success in authoring tasks validates its effectiveness, significantly advancing AR-HRI and fostering more comfortable and enhancing efficient human-centered interactions.

When applying high-level visual algorithms to rainy scenes, it is customary to preprocess the rainy images using low-level rain removal networks, followed by visual networks to achieve the desired objectives. Such a setting has never been explored by adversarial attack methods, which are only limited to attacking one kind of them. Considering the deficiency of multi-functional attacking strategies and the significance for open-world perception scenarios, we are the first to propose a Cascaded Adversarial Attack (CAA) setting, where the adversarial example can simultaneously attack different-level tasks, such as rain removal and semantic segmentation in an integrated system. Specifically, our attack on the rain removal network aims to preserve rain streaks in the output image, while for the semantic segmentation network, we employ powerful existing adversarial attack methods to induce misclassification of the image content. Importantly, CAA innovatively utilizes binary masks to effectively concentrate the aforementioned two significantly disparate perturbation distributions on the input image, enabling attacks on both networks. Additionally, we propose two variants of CAA, which minimize the differences between the two generated perturbations by introducing a carefully designed perturbation interaction mechanism, resulting in enhanced attack performance. Extensive experiments validate the effectiveness of our methods, demonstrating their superior ability to significantly degrade the performance of the downstream task compared to methods that solely attack a single network.

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient paradigm for the next generation of machine intelligence. However, the current focus within the SNN community prioritizes accuracy optimization through the development of large-scale models, limiting their viability in resource-constrained and low-power edge devices. To address this challenge, we introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials. By significantly compressing these two key elements, the proposed Q-SNNs substantially reduce both memory usage and computational complexity. Moreover, to prevent the performance degradation caused by this compression, we present a new Weight-Spike Dual Regulation (WS-DR) method inspired by information entropy theory. Experimental evaluations on various datasets, including static and neuromorphic, demonstrate that our Q-SNNs outperform existing methods in terms of both model size and accuracy. These state-of-the-art results in efficiency and efficacy suggest that the proposed method can significantly improve edge intelligent computing.

Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. Despite the success of some conditional methods, previous methods may neglect the importance of a sufficient formulation of task-specific condition strategy, resulting in suboptimal visual outcomes. In this study, we propose JoReS-Diff, a novel approach that incorporates Retinex- and semantic-based priors as the additional pre-processing condition to regulate the generating capabilities of the diffusion model. We first leverage pre-trained decomposition network to generate the Retinex prior, which is updated with better quality by an adjustment network and integrated into a refinement network to implement Retinex-based conditional generation at both feature- and image-levels. Moreover, the semantic prior is extracted from the input image with an off-the-shelf semantic segmentation model and incorporated through semantic attention layers. By treating Retinex- and semantic-based priors as the condition, JoReS-Diff presents a unique perspective for establishing an diffusion model for LLIE and similar image enhancement tasks. Extensive experiments validate the rationality and superiority of our approach.

ISP (Image Signal Processor) serves as a pipeline converting unprocessed raw images to sRGB images, positioned before nearly all visual tasks. Due to the varying spectral sensitivities of cameras, raw images captured by different cameras exist in different color spaces, making it challenging to deploy ISP across cameras with consistent performance. To address this challenge, it is intuitively to incorporate a raw-to-raw mapping (mapping raw images across camera color spaces) module into the ISP. However, the lack of paired data (i.e., images of the same scene captured by different cameras) makes it difficult to train a raw-to-raw model using supervised learning methods. In this paper, we aim to achieve ISP generalization by proposing the first unsupervised raw-to-raw model. To be specific, we propose a CSTPP (Color Space Transformation Parameters Predictor) module to predict the space transformation parameters in a patch-wise manner, which can accurately perform color space transformation and flexibly manage complex lighting conditions. Additionally, we design a CycleGAN-style training framework to realize unsupervised learning, overcoming the deficiency of paired data. Our proposed unsupervised model achieved performance comparable to that of the state-of-the-art semi-supervised method in raw-to-raw task. Furthermore, to assess its ability to generalize the ISP model across different cameras, we for the first formulated cross-camera ISP task and demonstrated the performance of our method through extensive experiments. The codes are released at https://github.com/ydxxxx/Unsupervised-Raw-to-raw-Mapping.

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Many existing approaches tend to assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. While these methods have succeeded, they may encounter challenges in practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods.

A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix. This matrix is decomposed into a product of down-projection and up-projection matrices, with the bottleneck dimensionality being crucial for reducing the number of learnable parameters, as exemplified by prevalent methods like LoRA and Adapter. However, these low-rank strategies typically employ a fixed bottleneck dimensionality, which limits their flexibility in handling layer-wise variations. To address this limitation, we propose a novel PEFT approach inspired by Singular Value Decomposition (SVD) for representing the adaptation matrix. SVD decomposes a matrix into the product of a left unitary matrix, a diagonal matrix of scaling values, and a right unitary matrix. We utilize Householder transformations to construct orthogonal matrices that efficiently mimic the unitary matrices, requiring only a vector. The diagonal values are learned in a layer-wise manner, allowing them to flexibly capture the unique properties of each layer. This approach enables the generation of adaptation matrices with varying ranks across different layers, providing greater flexibility in adapting pre-trained models. Experiments on standard downstream vision tasks demonstrate that our method achieves promising fine-tuning performance.

Biological systems possess remarkable sound source localization (SSL) capabilities that are critical for survival in complex environments. This ability arises from the collaboration between the auditory periphery, which encodes sound as precisely timed spikes, and the auditory cortex, which performs spike-based computations. Inspired by these biological mechanisms, we propose a novel neuromorphic SSL framework that integrates spike-based neural encoding and computation. The framework employs Resonate-and-Fire (RF) neurons with a phase-locking coding (RF-PLC) method to achieve energy-efficient audio processing. The RF-PLC method leverages the resonance properties of RF neurons to efficiently convert audio signals to time-frequency representation and encode interaural time difference (ITD) cues into discriminative spike patterns. In addition, biological adaptations like frequency band selectivity and short-term memory effectively filter out many environmental noises, enhancing SSL capabilities in real-world settings. Inspired by these adaptations, we propose a spike-driven multi-auditory attention (MAA) module that significantly improves both the accuracy and robustness of the proposed SSL framework. Extensive experimentation demonstrates that our SSL framework achieves state-of-the-art accuracy in SSL tasks. Furthermore, it shows exceptional noise robustness and maintains high accuracy even at very low signal-to-noise ratios. By mimicking biological hearing, this neuromorphic approach contributes to the development of high-performance and explainable artificial intelligence systems capable of superior performance in real-world environments.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed.

This paper addresses an important and valuable open-world object detection (OWOD) in autonomous driving scenarios, which aims to detect objects under bothdomain-agnosticandcategory-agnosticsettings simultaneously. Existing OWOD algorithms mainly focus on the detection of pre-defined object categories under various conditions (domain-agnostic) or instead perform zero-shot object detection (category-agnostic), separately. The knowledge gap between seen and unseen object categories poses challenges for models optimized with supervision from the only seen object categories. The domain difference across different scenarios also causes further challenges in aligning observations with different appearances. To address these two challenges simultaneously, we propose our Instance Dictionary Learning (IDL for short) for more robust and accurate OWOD performance. We first design a pre-training procedure to build up the mappings between region features and category semantic embeddings by introducing instance contrastive learning. The joint vision-semantic space is formulated through the more detailed instance-level “Dictionary”, which expresses the region-category correspondences and helps link the seen and unseen object categories. The domain discrimination is further designed for extracting the domain invariance feature representations in the further training procedure seamlessly. The proposed IDL could detect the unseen categories from unseen domains without any bounding box annotations while there is no obvious performance drop on detecting seen categories meanwhile. Comprehensive experiments have been conducted and our method could achieve a new state-of-the-art OWOD performance over previous algorithms.

Composed image retrieval aims to search a target image by concurrently understanding the composed inputs with a reference image and the complementary modification text. It aims to find a shared latent space where the representation of the composed inputs is close to the desired target image. Most previous methods capture the one-to-one correspondence between the composed inputs and target image, which encodes the composed inputs and the target image into single points in the feature space. However, the one-to-one correspondence cannot effectively handle this task due to the inherent ambiguity problem arising from the various semantic meanings and data uncertainty. Specifically, the composed inputs and target image always exhibit various semantic meanings, affecting the retrieval results. Moreover, given the composed inputs (resp. target image), there are multiple target images (resp. composed inputs) that equally make sense. In this paper, we propose a novel method termed Set of Diverse Queries with Uncertainty Regularization (SDQUR) to solve such inherent ambiguity problem. First, we utilize diverse queries to adaptively aggregate the composed inputs and target image into multiple deterministic embeddings that capture different semantic meanings in the triplet affecting the retrieval process. It can exploit the deterministic many-to-many correspondence within each triple through these set-based queries. Moreover, we provide an uncertainty regularization module to encode the composed inputs and target image into gaussian distribution. Multiple potential positive candidates are sampled from the distribution for probabilistic many-to-many correspondence. Through the complementary deterministic and probabilistic many-to-many correspondence manner, we achieve consistent improvements on the standard FashionIQ, CIRR, and Shoes benchmarks, surpassing the state-of-the-art methods by a large margin.

Mirror detection aims to discover mirror regions in images to avoid misidentifying reflected objects. Existing methods mainly mine clues from spatial domain. We observe that the frequencies inside and outside the mirror region are distinctive. Besides, the low-frequency representing the feature semantics can help to locate the mirror region, and the high-frequency representing the details can refine it. Motivated by this, we introduce frequency guidance and propose the dual domain perception progressive refinement network (DPRNet) to mine dual-domain information. Specifically, we first decouple the images into high-frequency and low-frequency components by Laplace pyramid and vision Transformer, respectively, and design the frequency interaction alignment (FIA) module to integrate frequency features to initially localize the mirror region. To handle scale variations, we propose the multi-order feature perception (MOFP) module to adaptively aggregate adjacent features with progressive and gating mechanisms. We further propose the separation-based difference fusion (SDF) module to establish associations between entities and imagings and discover the correct boundary to mine the complete mirror region. Extensive experiments show that DPRNet outperforms the state-of-the-art method by an average of 3% with only about one-fifth of the parameters and FLOPs on four datasets. Our DPRNet also achieves promising performance on remote sensing and camouflage scenarios, validating its generalization. The code is available athttps://github.com/winter-flow/DPRNet.

Recent research in cross-domain image retrieval has focused on addressing two challenging issues: handling domain variations in the data and dealing with the lack of sufficient training labels. However, these problems have often been studied separately, limiting the practicality and significance of the research outcomes. The existing cross-domain setting is also restricted to cases where domain labels are known during training, and all samples have semantic category information or instance correspondences. In this paper, we propose a novel approach to address a more general and practical problem:fully unsupervised domain-agnostic image retrievalunder the domain-unknown setting, where no annotations are provided. Our approach tackles both thedomain variationandmissing labelschallenges simultaneously. We introduce a new fully unsupervised One-Shot Synthesis-based Contrastive learning method (termed OSSCo) to project images from different data distributions into a shared feature space for similarity measurement. To handle the domain-unknown setting, we propose One-Shot unpaired image-to-image Translation (OST) between a randomly selected one-shot image and the rest of the training images. By minimizing the global distance between the original images and the generated images from OST, the model learns domain-agnostic representations. To address the label-unknown setting, we employ contrastive learning with a synthesis-based transform module from the OST training. This allows for effective representation learning without any annotations or external constraints. We evaluate our proposed method on diverse datasets, and the results demonstrate its effectiveness. Notably, our approach achieves comparable performance to current state-of-the-art supervised methods.

Cloud cover poses a significant challenge in remote sensing image processing, affecting the extraction and analysis of terrestrial features. Despite advancements in multitemporal cloud removal methods, single-image declouding remains crucial for emergency response and disaster management, where rapid acquisition of cloud-free imagery is essential. Traditional approaches often rely on synthetic aperture radar (SAR) or cloud masks as guidance for cloud removal, introducing additional complexities and dependencies on extensive data. To address these limitations, we propose a density-aware cloud removal using a global-local fusion Transformer (DCR-GLFT), which leverages density information as guidance and does not rely on extensive data. Specifically, our method employs density labels to guide the cloud removal process through two primary stages: cloud density estimation and density-guided cloud removal. A cloud density classifier is proposed in the first stage, trained with roughly estimated ground truth, to generate density labels for guiding subsequent removal processes. The second stage integrates cloud density information with cloud-ground image features using a Transformer-based network, enabling precise and nuanced cloud removal while preserving underlying surface details through the integration of both global and local features. The proposed method achieved the state-of-the-art results (peak signal-to-noise ratio (PSNR) of 28.93 and structural similarity index measure (SSIM) of 0.84) on the renowned cloud-removal dataset SEN12MS-CR, even without utilizing SAR data for guidance. This accomplishment highlights its significant advancement in the single-image cloud removal task. Our code will be made available athttps://github.com/ruiquan1214/DCR-GLFT.git.

In recent years, researchers have explored the use of sparse black-box video adversarial attacks, which involve selecting keyframes to reduce computational complexity and improve efficiency in generating perturbations. However, the current sparse strategy is not optimized for attack and detection steps, resulting in inaccurate frame selection. Some researchers have used reinforcement learning to train an agent to select keyframes, but this method requires additional training. To address these challenges, we propose a plug-and-play black-box sparse attack algorithm called CLVA based on the coreset concept of active learning. Our algorithm treats a video as a mini-dataset and employs the K-Center-Greedy algorithm to compute the distances between frames. We then select the frame that meets the distance condition as the key frame. We conducted extensive experiments using two attack algorithms on five mainstream recognition models and three video recognition datasets. Our results demonstrate that CLVA significantly accelerates the black-box video attack algorithm while achieving state-of-the-art performance in sparsity, time, and success rate compared to recent sparse attack algorithms. The implementation code of our CLVA method is available athttps://github.com/machineNo6/CLVA.

Semi-supervised Action Quality Assessment (AQA) using limited labeled and massive unlabeled samples to achieve high-quality assessment is an attractive but challenging task. The main challenge relies on how to exploit solid and consistent representations of action sequences for building a bridge between labeled and unlabeled samples in the semi-supervised AQA. To address the issue, we propose a Self-supervised sub-Action Parsing Network (SAP-Net) that employs a teacher-student network structure to learn consistent semantic representations between labeled and unlabeled samples for semi-supervised AQA. We perform actor-centric region detection and generate high-quality pseudo-labels in the teacher branch and assists the student branch in learning discriminative action features. We further design a self-supervised sub-action parsing solution to locate and parse fine-grained sub-action sequences. Then, we present the group contrastive learning with pseudo-labels to capture consistent motion-oriented action features in the two branches. We evaluate our proposed SAP-Net on four public datasets: the MTL-AQA, FineDiving, Rhythmic Gymnastics, and FineFS datasets. The experiment results show that our approach outperforms state-of-the-art semi-supervised methods by a significant margin.

Cross-modal retrieval (e.g., query a given image to obtain a semantically similar sentence, and vice versa) is an important but challenging task, as the heterogeneous gap and inconsistent distributions exist between different modalities. The dominant approaches struggle to bridge the heterogeneity by capturing the common representations among heterogeneous data in a constructed subspace which can reflect the semantic closeness. However, insufficient consideration is taken into the fact that learned latent representations are actually heavily entangled with those semantic-unrelated features, which obviously further compounds the challenges of cross-modal retrieval. To alleviate the difficulty, this work makes an assumption that the data are jointly characterized by two independent features: semantic-shared and semantic-unrelated representations. The former presents characteristics of consistent semantics shared by different modalities, while the latter reflects the characteristics with respect to the modality yet unrelated to semantics, such as background, illumination, and other low-level information. Therefore, this paper aims to disentangle the shared semantics from the entangled features, andthus the purer semantic representation can promote the closeness of paired data. Specifically, this paper designs a novel Semantics Disentangling approach for Cross-Modal Retrieval (termed as SDCMR) to explicitly decouple the two different features based on variational auto-encoder. Next, the reconstruction is performed by exchanging shared semantics to ensure the learning of semantic consistency. Moreover, a dual adversarial mechanism is designed to disentangle the two independent features via a pushing-and-pulling strategy. Comprehensive experiments on four widely used datasets demonstrate the effectiveness and superiority of the proposed SDCMR method by achieving a new bar on performance when compared against 15 state-of-the-art methods.

In the absence of an official statement about a rumor, people may expose the truth behind such rumor through their responses on social media. Due to the varying relevance of responses in exposing hidden suspicious points within a rumor claim, it is crucial to prioritize those with higher relevance, rather than considering every responding tweets. As for the multi-modal rumor detection, an effective approach for evaluating relevance is aligning responses with the different modalities of the rumor claim in a fine-grained manner. However, owing to the substantial volume of response tweets, it is both costly and redundant to align all responses with the multi-modal claim. In this paper, we propose a novel two-stage model, termedFocal Reasoning Model (FoRM), to select critical responses for multi-modal rumor detection. More specifically, our FoRM consists of two primary elements: coarse-grained selection and fine-grained reasoning. The coarse-grained selection component employs post-level features of responses to initialize a relevant score for each. Based on these scores, we preserve the responses with higher scores as the candidate ones for subsequent reasoning. Within the fine-grained reasoning component, we develop a relation attention module to investigate fine-grained relationships, specifically token-to-token and token-to-object connections, between the preserved responses and the multi-modal claim, with the goal of discovering valuable clues. Extensive experiments have been conducted on three real-world datasets, and the results demonstrate that our proposed model outperforms all the baselines.

With the explosive growth of multimedia contents, multimedia retrieval is facing unprecedented challenges on both storage cost and retrieval speed. Hashing technique can project the high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding. In the light of this, multi-modal hashing has recently received considerable attention to support large-scale multimedia retrieval. Different from uni-modal hashing, the multi-modal hashing focuses on modeling the multi-modal semantics and further preserving them into binary hash codes with hash learning. In this paper, we first systematically review the existing learning to hash methods for efficient multimedia retrieval, categorizing them according to the multimedia retrieval tasks, the specific multi-modal semantic modeling techniques, and hash learning strategies. Thereafter, we present the performance comparison results. We ultimately discuss the challenges and potential research directions that may require further investigation in multi-modal hash learning.

The susceptibility of deep neural networks (DNNs) to adversarial examples has raised significant concerns regarding the security and reliability of artificial intelligence systems. These examples contain maliciously crafted perturbations not perceptible to the human eye but can cause the model to make wrong predictions. Adversarial training (AT) is the de facto standard method for enhancing adversarial robustness. However, the improved robustness is often at the cost of a significant drop in standard accuracy for clean samples. Numerous works have attempted to alleviate this trade-off by identifying its causes. A key factor lies in the variability of clean samples, which leads to different adversarial examples being generated using the same attack strategy. The other factor is the disruption of the underlying data structure caused by adversarial perturbations. To overcome these challenges, we propose a novel adversarial training framework named Hardness-Guided Sample-Dependent Adversarial Training (HGSD-AT), which dynamically adjusts the attack strategy based on the hardness of the current adversarial sample to further improve the robustness of the model. By utilizing the two types of constraints which construct from a temporal perspective and spatial distribution perspective, our method directly learns the impact of attack methods on the model, rather than the indirect effects associated with sample distribution. This approach aims to improve the generation of adversarial examples while simultaneously enhancing the robustness and accuracy of DNNs. Our approach exhibits superior performance in terms of both robustness and natural accuracy compared to state-of-the-art defense methods, as validated through comprehensive experiments conducted on three benchmark datasets.

Multimodal sentiment analysis remains a big challenge due to the lack of effective fusion solutions. An effective fusion is expected to obtain the correct semantic representation for all modalities, and simultaneously thoroughly explore the contribution of each modality. In this paper, we propose a dominant SIngle-Modal SUpplementary Fusion (SIMSUF) approach to perform effective multimodal fusion for sentiment analysis. The SIMSUF is composed of three major components, a dominant modality supplementary module, a modality enhancement module, and a multimodal fusion module. The dominant modality supplementary module realizes dominant modality determination by estimating mutual dependence between every two modalities, and then the dominant modality is adopted to supplement other modalities for representative feature learning. To further explore the modality contribution, we propose a two-branch modality enhancement module, where one branch learns common representation distribution for multiple modalities, and simultaneously a specific modality enhancement branch is presented to perform semantic difference enhancement and distribution difference enhancement for each modality. Finally, a dominant modality leading fusion module is designed to fuse multimodal representations of two branches for sentiment analysis. Extensive experiments are evaluated on the CMU-MOSEI and CMU-MOSI datasets. Experiment results certify that our approach is superior to the state-of-the-art approaches. The source code of this work is available athttps://github.com/HumanCenteredUndestanding/SIMSUF.

Given an untrimmed video and a text query, Video Moment Retrieval (VMR) aims at retrieving a specific moment where the video content is semantically related to the text query. Conventional VMR methods rely on video-text paired data or specific temporal annotations for each target event. However, the subjectivity and time-consuming nature of the labeling process limit their practicality in multimedia applications. To address this issue, recently researchers proposed a Zero-Shot Learning setting for VMR (ZS-VMR) that trains VMR models without manual supervision signals, thereby reducing the data cost. In this paper, we tackle the challenging ZS-VMR problem withAngular Reconstructive Text embeddings (ART), generalizing the image-text matching pre-trained model CLIP to the VMR task. Specifically, assuming that visual embeddings are close to their semantically related text embeddings in angular space, our ART method generates pseudo-text embeddings of video event proposals through the hypersphere of CLIP. Moreover, to address the temporal nature of videos, we also design local multimodal fusion learning to narrow the gaps between image-text matching and video-text matching. Our experimental results on two widely used VMR benchmarks, Charades-STA and ActivityNet-Captions, show that our method outperforms current state-of-the-art ZS-VMR methods. It also achieves competitive performance compared to recent weakly-supervised VMR methods.

The existing face forgery algorithms have achieved remarkable progress in how to generate reasonable facial images and can even successfully deceive human beings. Considering public security, face forgery detection is of vital importance, making it essential to design face forgery detection algorithms to detect forgery images over the Internet. Despite the great success achieved by the existing Deepfake detection algorithms, they usually failed to achieve satisfactory Deepfake detection performance when deployed to handle the forgery videos in practice. One significant reason is compression. The videos over the Internet are inevitably compressed considering the transmission efficiency. To address this issue, in this paper, we propose a generic, simple yet effective “bleaching” pre-processing module based on the generative model and the high-level feature representations to produce ableached image, which shares a similar appearance with the compressed images. The bleached images with recovered information can be identified accurately by the optimized Deepfake detection models without retraining. The proposed method has utilized a redesigned feature representation, which serves as a navigator to effectively and sufficiently alter the feature distribution in the high-dimensional space to remedy the difference between real facial images and forgery counterparts. Thus, the proposed method can successfully avoid misclassification. Comprehensive and extensive experiments are carried out on four low-quality Faceforensics++ datasets, demonstrating the effectiveness of our method in recovering the information loss caused by the compression artifacts across various backbones and compression.

Multi-view speech emotion recognition (SER) based on the pre-trained model has gained attention in the last two years, which shows great potential in improving the model performance in speaker-independent scenarios. However, the existing work either relies on various fine-tuning methods or uses excessive feature views with complex fusion strategies, causing the increase of complexity with limited performance benefit. In this paper, we improve multi-view SER based on the pre-trained model from the perspective of a low-level speech feature. Specifically, we forgo fine-tuning the pre-trained model and instead focus on learning effective features hidden in the low-level speech feature mel-scale frequency cepstral coefficient (MFCC). We propose atwo-streampoolingchannelattention (TsPCA) module to discriminatively weight the channel dimensions of the features derived from MFCC. This module enables inter-channel interaction and learning of emotion sequence information across channels. Furthermore, we design a simple but effective feature view fusion strategy to learn robust representations. In the comparison experiments, our method achieves the WA and UA of 73.97%/74.69% and 74.61%/75.66% on the IEMOCAP dataset, 97.21% and 97.11% on the Emo-DB dataset, 77.08% and 77.34% on the RAVDESS dataset, and 74.38% and 71.43% on the SAVEE dataset. Extensive experiments on the four datasets demonstrate that our method consistently surpasses existing methods and achieves a new State-of-the-Art result.

The vulnerability of deep neural networks to adversarial examples has raised huge concerns about the security of these algorithms. Black-box adversarial attacks have received a lot of attention as an influential method for evaluating model robustness. While various sophisticated adversarial attack methods have been proposed, the success rate in the black-box scenario still needs to be improved. To address these issues, we develop an Adaptive Multi-scale Degradation-based Attack method calledAMDA. The intuitive motivation behind our approach is that different models tend to have similar attention regions for low-scale images. Specifically, AMDA uses degraded images to generate perturbations at different scales and fuses these perturbations to generate adversarial examples that are insensitive to model changes. Furthermore, we design an adaptive multi-scale perturbation fusion that evaluates the transferability of perturbations at different scales based on noise and adaptively allocates fusion weights to prioritize strong transferability attacks and avoid being compromised by local optima. Extensive experimental results on the ImageNet, CIFAR-100, and CIFAR-10 datasets demonstrate that the proposed AMDA algorithm exhibits competitive performance for both normally trained models and defense models.

Based on deterministic single-point embedding, most extant image-text retrieval methods only focus on the match of ground truth while suffering from one-to-many correspondence, where besides annotated positives, many similar instances of another modality should be retrieved by a given query. Recent solutions of probabilistic embedding and rectangle mapping still encounter some drawbacks, albeit their promising effectiveness at multiple matches. Meanwhile, the exploration of one-to-many correspondence is still insufficient. Therefore, this paper proposes a novel geometric representation toEstimate theSemantics of heterogeneous data viaSectorEmbedding (dubbed ESSE). Specifically, a given image/text can be projected as a sector, where its symmetric axis represents mean semantics and the aperture estimates uncertainty. Further, a sector matching loss is introduced to better handle the multiplicity by considering the sine of included angles as distance calculation, which encourages candidates to be contained by the apertures of a query sector. The experimental results on three widely used benchmarks CUB, Flickr30K and MS-COCO reveal that sector embedding can achieve competitive performance on multiple matches and also improve the traditional ground-truth matching of the baselines. Additionally, we also verify the generalization to video-text retrieval on two extensively used datasets of MSRVTT and MSVD, and to text-based person retrieval on CUHK-PEDES. This superiority and effectiveness can also demonstrate that the bounded property of the aperture can better estimate semantic uncertainty when compared to prior remedies.

Recently, person re-identification has gained significant attention from both academic and industry fields due to its potential applications in surveillance and security. However, the security of re-identification systems has not been widely investigated, and they are vulnerable to adversarial attacks, which can significantly degrade their performance. Although numerous sophisticated adversarial training methods have been proposed for image classification, metric analysis systems such as person re-identification have not been fully explored. In this paper, we develop a novel adversarial training framework with a dynamic attack strategy for person re-identification, to further enhance the robustness of the model. Specifically, we gradually increase the perturbation budget during the generation until the generated adversarial examples reach a certain level of attack strength. As the iterations progress, the model becomes more robust, and our framework can generate stronger adversarial examples to continuously explore the robustness bounds of the model. Moreover, to alleviate the conflict between the adversarial robustness and natural generalization of the model, we design a novel performance alignment loss to further constrain the adversarial example generation process, which can make the generated adversarial examples as close as possible to the clean samples in terms of performance. Experiments on two widely used person re-ID benchmark datasets demonstrate the effectiveness and superiority of our proposed method.

Deep Neural Networks (DNNs) have primarily been demonstrated to be successful when large-scale labeled data are available. However, DNNs usually fail when tasked in few-sample learning scenarios, and the results will be much worse when the limited data show large intra-class variation and inter-class similarity (a.k.a fine-grained classification). To solve this challenging task, the idea of carrying out feature augmentation is visited and better achieved by exploring the merit of the forward Euler method in solving ordinary differential equations (ODEs), and a novel high-order feature augmentation (HFA) model with ResNet is proposed. Specifically, the proposed method leverages the stacked residual structure to model the direction of feature change over the initial state, and uses the triplet loss as constraint to model the step size of change in an adaptive manner. As a result, the initial features can then be augmented by a residual structure with a forward Eulerian form to generate features of the same subcategory with a similar representation as the input image. Furthermore, the proposed augmentation mechanism enjoys two additional benefits: a) it can help avoid the over-fitting issue when learned with insufficient training data; b) it can be used seamlessly with any residual structure-based classification network, and the ResNet used in this paper remains unchanged during testing. Extensive experiments are carried out on fine-grained visual categorization benchmarks, and the results demonstrate that our approach can significantly improve the categorization performance when the training data is highly insufficient.

We study the task of image retrieval with text feedback, where a reference image and modification text are composed to retrieve the desired target image. To accomplish this goal, existing methods always get the multimodal representations through different feature encoders and then adopt different strategies to model the correlation between the composed inputs and the target image. However, the multimodal query brings more challenges as it requires not only the synergistic understanding of the semantics from the heterogeneous multimodal inputs but also the ability to accurately build the underlying semantic correlation existing in each inputs-target triplet, i.e., reference image, modification text, and target image. In this paper, we tackle these issues with a novel Align and Retrieve (AlRet) framework. First, our proposed methods employ the contrastive loss in the feature encoders to learn meaningful multimodal representation while making the subsequent correlation modeling process in a more harmonious space. Then we propose to learn the accurate correlation between the composed inputs and target image in a novel composition-and-decomposition paradigm. Specifically, the composition network couples the reference image and modification text into a joint representation to learn the correlation between the joint representation and target image. The decomposition network conversely decouples the target image into visual and text subspaces to exploit the underlying correlation between the target image with each query element. The composition-and-decomposition paradigm forms a closed loop, which can be optimized simultaneously to promote each other in the performance. Massive comparison experiments on three real-world datasets confirm the effectiveness of the proposed method.

Given an input image, scene graph generation (SGG) aims to generate comprehensive visual relationships between objects in the form of graphs. Recently, more attention to the design of complex networks and complicated strategies has been paid to the long tail issue caused by the imbalanced class distribution. However, most existing methods adopt the concatenated features of two objects in real space as the final relation representation for a given triplet. We mainly argue that such a simple concatenation may neglect the importance of complex interactions between objects, which results in the diversity of visual relations. In addition, the representation learning in real space is also inadequate to express this property. To alleviate these issues, we seamlessly incorporate Hermitian inner product into existing models to facilitate the generation of scene graphs by learning Relation Embedding in Complex space (CoRE). More specifically, we first introduce the concept of complex-valued representations for entities and then formulate the relation triplets with Hermitian inner product in complex space. Finally, we investigate the effect of utilizing only real component or both of Hermitian inner product on inferring more reasonable interaction between objects for scene graphs. Comprehensive experiments on two widely used benchmark datasets, Visual Genome (VG) and Open Image, demonstrate our effectiveness, superiority, and generalization on various metrics for biased or unbiased inference.

Rumor verification on social media aims to identify the truth value of a rumor, which is important to decrease the detrimental public effects. A rumor might arouse heated discussions and replies, conveying different stances of users that could be helpful in identifying the rumor. Thus, several works have been proposed to verify a rumor by modelling its entire stance sequence in the time domain. However, these works ignore that such a stance sequence could be decomposed into controversies with different intensities, which could be used to cluster the stance sequences with the same consensus. In addition, the existing stance extractors fail to consider both the impact of all previously posted tweets and the reply chain on obtaining the stance of a new reply. To address the above problems, in this article, we propose a novel stance-based network to aggregate the controversies of the stance sequence for rumor verification, termed Filter-based Stance Network (FSNet). As controversies with different intensities are reflected as the different changes of stances, it is convenient to represent different controversies in the frequency domain, but it is hard in the time domain. Our proposed FSNet decomposes the stance sequence into multiple controversies in the frequency domain and obtains the weighted aggregation of them. Specifically, FSNet consists of two modules: the stance extractor and the filter block. To obtain better stance features toward the source, the stance extractor contains two stages. In the first stage, the tweet representation of each reply is obtained by aggregating information from all previously posted tweets in a conversation. Then, the features of stance toward the source, i.e., rumor-aware stance, are extracted with the reply chains in the second stage. In the filter block module, a rumor-aware stance sequence is constructed by sorting all the tweets of a conversation in chronological order. Fourier Transform thereafter is employed to convert the stance sequence into the frequency domain, where different frequency components reflect controversies of different intensities. Finally, a frequency filter is applied to explore the different contributions of controversies. We supervise our FSNet with both stance labels and rumor labels to strengthen the relations between rumor veracity and crowd stances. Extensive experiments on two benchmark datasets demonstrate that our model substantially outperforms all the baselines.

Although deep convolutional neural networks have achieved satisfactory performance in many medical image segmentation tasks, a considerable annotation challenge still needs to be solved, which is expensive and time-consuming for radiologists. Most existing popular semi-supervised methods mainly impose data-level perturbations (e.g., rotation, noising) or feature-level perturbations (e.g., MC dropout) on unlabeled data. In this paper, we propose a novel semi-supervised segmentation strategy with meaningful perturbations at the feature level to leverage abundant useful information naturally embedded in the unlabeled data. Specifically, we develop a dual-task network where the segmentation head produces multiple predictions with a perturbation module, and the reconstruction head further utilizes the semantic information to enhance segmentation performance. The proposed framework subtly perturbs the network at the feature-level to generate predictions which should be similar and consistent. However, enforcing them roughly to be consistent at all pixels harms stable training and neglects much delicate information. To better utilize those predictions and estimate the uncertainty, we further propose feature-perturbed consistency to exploit reliable regions for our framework to learn from. Extensive experiments on the public BraTS2020 dataset and the 2017 ACDC dataset confirm the efficiency and effectiveness of our method. In particular, the proposed method demonstrates remarkable superiority in the segmentation of boundary regions. The project is available at https://github.com/youngyzzZ/SFPC.

Image-text retrieval is a fundamental task to bridge vision and language by exploiting various strategies to finegrained alignment between regions and words. This is still tough mainly because of one-to-many correspondence, where a set of matches from another modality can be accessed by a random query. While existing solutions to this problem including multi-point mapping, probabilistic distribution, and geometric embedding have made promising progress, one-to-many correspondence is still under-explored. In this work, we develop a Multilateral Semantic Relations Modeling (termed MSRM) for image-text retrieval to capture the one-to-many correspondence between multiple samples and a given query via hypergraph modeling. Specifically, a given query is first mapped as a probabilistic embedding to learn its true semantic distribution based on Mahalanobis distance. Then each candidate instance in a mini-batch is regarded as a hypergraph node with its mean semantics while a Gaussian query is modeled as a hyperedge to capture the semantic correlations beyond the pair between candidate points and the query. Comprehensive experimental results on two widely used datasets demonstrate that our MSRM method can outper-form state-of-the-art methods in the settlement of multiple matches while still maintaining the comparable performance of instance-level matching.

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement.

Existing MWP solvers employ sequence or binary tree to present the solution expression and decode it from given problem description.However, such structures fail to handle the variants that can be derived via mathematical manipulation, e.g., (a 1 + a 2 ) * a 3 and a 1 * a 3 +a 2 * a 3 can both be possible valid solutions for a same problem but formulated as different expression sequences or trees.The multiple solution variants depicting different possible solving procedures for the same input problem would raise two issues: 1) making it hard for the model to learn the mapping function between the input and output spaces effectively, and 2) wrongly indicating wrong when evaluating a valid expression variant.To address these issues, we introduce a unified tree structure to present a solution expression, where the elements are permutable and identical for all the expression variants.We propose a novel non-autoregressive solver, named MWP-NAS, to parse the problem and deduce the solution expression based on the unified tree.For evaluating the possible expression variants, we design a path-based metric to evaluate the partial accuracy of expressions of a unified tree.The results from extensive experiments conducted on Math23K and MAWPS demonstrate the effectiveness of our proposed MWP-NAS.The codes and checkpoints are available at: https: //github.com/mengqunhan/MWP-NAS.

Sounding source localization is a challenging cross-modal task due to the difficulty of crossmodal alignment.Although supervised crossmodal methods achieve encouraging performance, heavy manual annotations are expensive and inefficient.Thus it is valuable and meaningful to develop unsupervised solutions.In this paper, we propose an Unsupervised Sounding Pixel Learning (USPL) approach which enables a pixel-level sounding source localization in unsupervised paradigm.We first design a mask augmentation based multiinstance contrastive learning to realize unsupervised cross-modal coarse localization, which aligns audio-visual features to obtain coarse sounding maps.Secondly, we present an Unsupervised Sounding Map Refinement (SMR) module which employs the visual semantic affinity learning to explore inter-pixel relations of adjacent coordinate features.It contributes to recovering the boundary of coarse sounding maps and obtaining fine sounding maps.Finally, a Sounding Pixel Segmentation (SPS) module is presented to realize audio-supervised semantic segmentation.Extensive experiments are performed on the AVSBench-S4 and VG-GSound datasets, exhibiting encouraging results compared with previous SOTA methods.

In this paper, we introduce a novel structure-aware method to generate diverse, and realistic 3D shapes via semantic parts. Most previous works neglect the structural and context information between shape parts and only consider the geometric information. This sometimes leads to the wrong combination of parts during the generation process and brought down the generation quality. To address this issue, we learn a structure-aware latent representation for 3D shapes by training a variational autoencoder(VAE). Specially, we use a graph to express semantic parts and their structural relationship of the 3D shape. Based on that graph representation, we design a generative network based on graph transformer architecture, called Graph Transformer VAE networks(GT-Net), to encode and decode the graph-represented 3D shape. Our experimental results demonstrate that our method achieves better performance than previous methods among various shape families, especially in terms of capturing shape details information.

As acquiring pixel-wise labels for semantic segmentation is labor-intensive, unsupervised domain adaptation (UDA) techniques aim to transfer knowledge from synthetic data to real-scene data. To overcome the distribution misalignment between the source domain and the target domain, Teacher-Student (TS) methods are widely-used and promising. In TS methods, the student resorts to the one-hot pseudo labels generated by the teacher. However, the generated one-hot pseudo labels are dubious and ignore the semantic correlation among classes. Besides, in the same position of the same image, the output distributions between the student and the teacher should be consistent. Such prediction consistency is defined as Region-Aware Semantic Consistency (RASC). Correspondingly, we propose an RASC module to assimilate the output distributions of the teacher and the student. Our RASC module is flexible and easily plugged into TS state-of-the-arts (SOTAs) based on either CNNs or Transformers.

Text-based person retrieval is one of the fundamental tasks in the field of computer vision, which aims to retrieve the most relevant pedestrian image from all the candidates according to textual descriptions. Such a cross-modal retrieval task could be challenging since it requires one to properly select distinguishing clues and perform cross-modal alignments. To achieve cross-modal alignments, most previous works focus on different inter-modal constraints while overlooking the influence of intra-modal noise, yielding sub-optimal retrieved results in certain cases. To this end, we propose a novel framework termed Multi-granularity Separation Network with Bidirectional Refinement Regularization (MSN-BRR) to tackle the problem. The framework consists of two components: (1) Multi-granularity Separation Network, which extracts the multi-grained discriminative textual and visual representations at local and global semantic levels. (2) Bidirectional Refinement Regularization, which alleviates the influence of intra-modal noise and facilitates the proper alignments between the visual and textual representations. Extensive experiments on two widely used benchmarks, i.e., CUHK-PEDES and ICFG-PEDES show that our MSN-BRR method outperforms current state-of-the-art methods.

Zero-shot sketch-based image retrieval (ZS-SBIR) is a task that learns semantic knowledge and embedding extraction to retrieve similar images using a sketch without any training examples of unseen classes. Existing methods have attempted to address the modal and semantic gaps in ZS-SBIR by using various strategies such as leveraging category linguistic information for improved discriminability and utilizing knowledge distillation to increase the model’s generalizability towards unseen classes. However, these methods fail to consider the importance of discriminability and generalizability in a unified manner. To address this, we propose a novel method called Adaptive Balanced Discriminability and Generalizability (ABDG) for ZS-SBIR. Specifically, our ABDG method utilizes an advanced two-stage knowledge distillation scheme to balance the learning of discriminability and generalizability for each instance. In addition to task-agnostic teacher models to preserve structural information used in existing work, we introduce a task-specific teacher model pre-trained with a classification objective function to emphasize the discriminability property during knowledge distillation. We also employ a novel entropy-based weighting strategy to balance the effects of structural information preservation and classification losses specific to the classification progress of each instance. Furthermore, we use fine-grained semantic relevance to refine the ego predictions of the student model, with the aim of improving its performance as the training objective continues to converge. Experimental results on three benchmark datasets of ZS-SBIR demonstrate that our ABDG method establishes a state-of-the-art performance by balancing the learning of discriminative and generalizable properties.

Most existing cross-modal retrieval methods employ two-stream encoders with different architectures for images and texts, e.g., CNN for images and RNN/Transformer for texts. Such discrepancy in architectures may induce different semantic distribution spaces and limit the interactions between images and texts, and further result in inferior alignment between images and texts. To fill this research gap, inspired by recent advances of Transformers in vision tasks, we propose to unify the encoder architectures with Transformers for both modalities. Specifically, we design a cross-modal retrieval framework purely based on two-stream Transformers, dubbed Hierarchical Alignment Transformers (HAT), which consists of an image Transformer, a text Transformer, and a hierarchical alignment module. With such identical architectures, the encoders could produce representations with more similar characteristics for images and texts, and make the interactions and alignments between them much easier. Besides, to leverage the rich semantics, we devise a hierarchical alignment scheme to explore multi-level correspondences of different layers between images and texts. To evaluate the effectiveness of the proposed HAT, we conduct extensive experiments on two benchmark datasets, MSCOCO and Flickr30K. Experimental results demonstrate that HAT outperforms SOTA baselines by a large margin. Specifically, on two key tasks, i.e., image-to-text and text-to-image retrieval, HAT achieves 7.6% and 16.7% relative score improvement of Recall@1 on MSCOCO, and 4.4% and 11.6% on Flickr30k respectively. The code is available at https://github.com/LuminosityX/HAT.

Effective alignment and fusion of multimodal features remain a significant challenge for multimodal sentiment analysis. In various multimodal applications, the text modal exhibits a significant advantage of compact yet expressive representation ability. In this paper, we propose a Cross-modality Representation Interactive Learning (CRIL) approach, which adopts the text modality to guide other modalities for learning representative feature tokens, contributing to effective multimodal fusion in multimodal sentiment analysis. We propose a semantic representation interactive learning module to learn concise semantic representation tokens for audio and video modalities under the guidance of the text modality, ensuring semantic alignment of representations among multiple modalities. Furthermore, we design a semantic relationship interactive learning module, which calculates a self-attention matrix for each modality and controls their consistency to enable the semantic relationship alignment for multiple modalities. Finally, we present a two-stage interactive fusion solution to bridge the modality gap for multimodal fusion and sentiment analysis. Extensive experiments are performed on the CMU-MOSEI, CMU-MOSI, and UR-FUNNY datasets, and experiment results demonstrate the effectiveness of our proposed approach.

Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an untrimmed video with natural language queries. Existing VMR methods suffer from two defects: (1) massive expensive temporal annotations are required to obtain satisfying performance; (2) complicated cross-modal interaction modules are deployed, which lead to high computational cost and low efficiency for the retrieval process. To address these issues, we propose a novel method termed Cheaper and Faster Moment Retrieval (CFMR), which balances the retrieval accuracy, efficiency, and annotation cost for VMR. Specifically, our proposed CFMR method learns from point-level supervision where each annotation is a single frame randomly located within the target moment. Such a labeling strategy achieves 6 times cheaper than the conventional annotations of event boundaries. Furthermore, we also design a concept-based multimodal alignment mechanism to bypass the usage of cross-modal interaction modules during the inference process, remarkably improving retrieval efficiency. The experimental results on three widely used VMR benchmarks demonstrate our proposed CFMR method achieves superior comprehensive performance to current state-of-the-art methods. Moreover, it significantly accelerates the retrieval speed with more than 100 times FLOPs compared to existing approaches with point-level supervision. Our open-source implementation is available at https://github.com/CFM-MSG/Code_CFMR.

Text-based person retrieval aims at searching for a pedestrian image from multiple candidates with textual descriptions. It is challenging due to uncertain cross-modal alignments caused by the large intra-class variations. To address the challenge, most existing approaches rely on various attention mechanisms and auxiliary information, yet still struggle with the uncertain cross-modal alignments arising from significant intra-class variation, leading to coarse retrieval results. To this end, we propose a novel framework termed Deep Cross-modal Evidential Learning (DCEL), which deploys evidential deep learning to consider the cross-modal alignment uncertainty. Our DCEL model comprises three components: (1) Bidirectional Evidential Learning, which models alignment uncertainty to measure and mitigate the influence of large intra-class variation; (2) Multi-level Semantic Alignment, which leverages a proposed Semantic Filtration module and image-text similarity distribution to facilitate cross-modal alignments; (3) Cross-modal Relation Learning, which reasons about latent correspondences between multi-level tokens of image and text. Finally, we integrate the advantages of the three proposed components to enhance the model to achieve reliable cross-modal alignments. Our DCEL method consistently outperforms more than ten state-of-the-art methods in supervised, weakly supervised, and domain generalization settings on three benchmarks: CUHK-PEDES, ICFG-PEDES, and RSTPReid.

Most existing image-text matching methods adopt triplet loss as the optimization objective, and choosing a proper negative sample for the triplet of is important for effectively training the model, e.g., hard negatives make the model learn efficiently and effectively. However, we observe that existing methods mainly employ the most similar samples as hard negatives, which may not be true negatives. In other words, the samples with high similarity but not paired with the anchor may reserve positive semantic associations, and we call them false negatives. Repelling these false negatives in triplet loss would mislead the semantic representation learning and result in inferior retrieval performance. In this paper, we propose a novel False Negative Elimination (FNE) strategy to select negatives via sampling, which could alleviate the problem introduced by false negatives. Specifically, we first construct the distributions of positive and negative samples separately via their similarities with the anchor, based on the features extracted from image and text encoders. Then we calculate the false negative probability of a given sample based on its similarity with the anchor and the above distributions via the Bayes' rule, which is employed as the sampling weight during negative sampling process. Since there may not exist any false negative in a small batch size, we design a memory module with momentum to retain a large negative buffer and implement our negative sampling strategy spanning over the buffer. In addition, to make the model focus on hard negatives, we reassign the sampling weights for the simple negatives with a cut-down strategy. The extensive experiments are conducted on Flickr30K and MS-COCO, and the results demonstrate the superiority of our proposed false negative elimination strategy. The code is available at https://github.com/LuminosityX/FNE.

Existing domain adaptive object detection algorithms (DAOD) have demonstrated their effectiveness in discriminating and localizing objects across scenarios. However, these algorithms typically assume a single source and target domain for adaptation, which is not representative of the more complex data distributions in practice. To address this issue, we propose a novel Open-Scenario Domain Adaptive Object Detection (OSDA), which leverages multiple source and target domains for more practical and effective domain adaptation. We are the first to increase the granularity of the background category by building the foundation model using contrastive vision-language pre-training in an open-scenario setting for better distinguishing foreground and background, which is under-explored in previous studies. The performance gains by introducing the pre-training have been observed and have validated the model's ability to detect objects across domains. To further fine-tune the model for domain-specific object detection, we propose a hierarchical feature alignment strategy to obtain a better common feature space among the various source and target domains. In the case of multi-source domains, the cross-reconstruction framework is introduced for learning more domain invariances. The proposed method is able to alleviate knowledge forgetting without any additional computational costs. Extensive experiments across different scenarios demonstrate the effectiveness of the proposed model.

Multimodal physiological-based emotion recognition is one of the most available but challenging studies due to complexity of emotions and individual differences in physiological signals. However, existing studies mainly combine multimodal data to fuse multimodal information in offline scenarios, ignoring data/modalities correlation among multimodal data and individual differences of non-stationary physiological signals in online scenarios. In this paper, we propose a novel Online Multimodal HyperGraph Learning (OMHGL) method to fuse multimodal information for emotion recognition based on time-series physiological signals. Our method consists of multimodal hypergraph fusion and online hypergraph learning. Specifically, the multimodal hypergraph fusion can fuse multimodal physiological signals to effectively obtain emotionally dependent information via leveraging multimodal information and higher-order correlations among multimodal data/modalities. The online hypergraph learning is designed to learn new information from online data by updating hypergraph projection. As a result, the proposed online emotion recognition model can be more effective for emotion recognition of target subjects when target data arrive in an online manner. Experimental results have demonstrated that the proposed method significantly outperforms the baselines and compared state-of-the-art methods in online emotion recognition tasks.

An ideal multi-modal recommendation system is supposed to be timely updated with the latest modality information and interaction data because the distribution discrepancy between new data and historical data will lead to severe recommendation performance deterioration. However, upgrading a recommendation system with numerous new data consumes much time and computing resources. To mitigate this problem, we propose a Task-Adversarial Adaptation (TAA) framework, which is able to align data distributions and reduce resource consumption at the same time. This framework is specifically designed to align distributions of embedded features for different recommendation tasks between the source domain (i.e., historical data) and the target domain (i.e., new data). Technically, we design a domain feature discriminator for each task to distinguish which domain a feature comes from. By the two-player min-max game between the feature discriminator and the feature embedding network, the feature embedding network is able to align the source and target data distributions. With the ability to align source and target distributions, we are able to reduce the number of training samples by random sampling. In addition, we formulate the proposed approach as a plug-and-play module to accelerate the model training and improve the performance of mainstream multi-modal multi-task recommendation systems. We evaluate our method by predicting the Click-Through Rate (CTR) in e-commerce scenarios. Extensive experiments verify that our method is able to significantly improve prediction performance and accelerate model training on the target domain. For instance, our method is able to surpass the previous state-of-the-art method by 2.45% in terms of Area Under Curve (AUC) on AliExpress_US dataset while only utilizing one percent of the target data in training. Code: https://github.com/TL-UESTC/TAA.

Continual test-time domain adaptation (TTA) is a challenging topic in the field of source-free domain adaptation, which focuses on addressing cross-domain multimedia information during inference with a continuously changing data distribution. Previous methods have been found to lack noise robustness, leading to a significant increase in errors under strong noise. In this paper, we address the noise-robustness problem in continual TTA by offering three effective recipes to mitigate it. At the category level, we employ the Taylor cross-entropy loss to alleviate the low confidence category bias commonly associated with cross-entropy. At the sample level, we reweight the target samples based on uncertainty to prevent the model from overfitting on noisy samples. Finally, to reduce pseudo-label noise, we propose a soft ensemble negative learning mechanism to guide the model optimization using ensemble complementary pseudo labels. Our method achieves state-of-the-art performance on three widely used continual TTA datasets, particularly in the strong noise setting that we introduced.

Using a Graph convolution network (GCN) for constructing and aggregating node features has been helpful for skeleton-based action recognition. The strength of the nodes' relation of an action sequence distinguishes it from other actions. This work proposes a novel spatial module called Multi-scale self-relational graph convolution (MS-SRGC) for dynamically modeling joint relations of action instances. Modeling the joints' relations is crucial in determining the spatial distinctiveness between skeleton sequences; hence MS-SRGC shows effectiveness for activity recognition. We also propose a Hybrid multi-scale temporal convolution network (HMS-TCN) that captures different ranges of time steps along the temporal dimension of the skeleton sequence. In addition, we propose a Spatio-temporal blackout (STB) module that randomly zeroes some continue frames for selected strategic joint groups. We sequentially stack our spatial (MS-SRGC) and temporal (HMS-TCN) modules to form a Self-relational graph convolution network (SR-GCN) block, which we use to construct our SR-GCN model. We append our STB on the SR-GCN model top for the randomized operation. With the effectiveness of ensemble networks, we perform extensive experiments on single and multiple ensembles. Our results beat the state-of-the-art methods on the NTU RGB-D, NTU RGB-D 120, and Northwestern-UCLA datasets.

Deep hashing has been widely applied in large-scale image retrieval due to its powerful computational efficiency. Nevertheless, the vulnerability of deep hashing to adversarial examples has been revealed, particularly to targeted attacks with stronger manipulability. Existing targeted attack methods for deep hashing default to selecting target labels from random images, usually encompassing multiple classes for attack in multi-label datasets. However, they exhabit poor performance when facing a preciser single target label selection. In this work, we propose a novel Precise Target-Oriented Attack dubbed PTA, to enhance the precision of such targeted attacks. Specifically, we further categorize the general target label into preciser single target label for attack. By relaxing the non-differentiable indicator function, we directly adopt Average Precision (AP) as optimization objective to guide the generation of adversarial examples on a small subset of the entire database, thus achieving stronger precision. Extensive experiments demonstrate that the proposed PTA achieves state-of-the-art performance in both general and single target label selection, with superior transferability and universality.

Previous studies on multimodal fake news detection have observed the mismatch between text and images in the fake news and attempted to explore the consistency of multimodal news based on global features of different modalities. However, they fail to investigate this relationship between fine-grained fragments in multimodal content. To gain public trust, fake news often includes relevant parts in the text and the image, making such multimodal content appear consistent. Using global features may suppress potential inconsistencies in irrelevant parts. Therefore, in this paper, we propose a novel Consistency-learning Fine-grained Fusion Network (CFFN) that separately explores the consistency and inconsistency from high-relevant and low-relevant word-region pairs. Specifically, for a multimodal post, we divide word-region pairs into high-relevant and low-relevant parts based on their relevance scores. For the high-relevant part, we follow the cross-modal attention mechanism to explore the consistency. For low-relevant part, we calculate inconsistency scores to capture inconsistent points. Finally, a selection module is used to choose the primary clue (consistency or inconsistency) for identifying the credibility of multimodal news. Extensive experiments on two public datasets demonstrate that our CFFN substantially outperforms all the baselines. Our code can be found at: https://github.com/uestc-lj/CFFN/.

The huge domain gap between sketches and photos poses huge challenges for Sketch-Based Image Retrieval (SBIR). The Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is more generic and practical but brings an even greater challenge: the additional knowledge gap between the seen and unseen categories. In order to simultaneously mitigate both gaps, we propose an Approaching-and-Centralizing Network (termed “ACNet”) to jointly optimize sketch-to-photo synthesis and image retrieval. The retrieval module guides the synthesis module to generate large amounts of diverse photo-like images that help the sketch domain gradually approach the photo domain to eliminate the domain gap, and thus better serves retrieval. Meanwhile, the retrieval module itself centralizes the embeddings of training samples for learning a similarity measurement to eliminate the knowledge gap. Our approach is simple yet effective, which achieves state-of-the-art performance on two widely used ZS-SBIR datasets and surpasses previous methods by a large margin (eg, 8.2% improvement in terms of mAP@all on TU-Berlin Extended dataset).

Deep Metric Learning (DML) is very effective for many computer vision applications such as image retrieval or cross-modal matching. The common paradigm for DML is to seek metric spaces that can encode semantically similar objects close while locating the dissimilar ones far away from each other. To make features more discriminative, the mainstream methods usually design various specific loss functions to seek the help of hard negatives through complex hard mining strategies or hard synthesizing with additional networks. In spite of their fruitfulness, these approaches ignore the impact of low-level information in images on the performance, which may degrade the discerning ability of learned embedding. To alleviate these problems, we introduce a simple yet effective augmentation method to generate more hard negatives by swapping the low-frequency spectra of negative instances with anchors in the Fourier domain. Specifically, unlike previous methods, our proposed approach does not involve any complex design strategies but enriches hard negatives by manipulating the low-level variability of images only with simple Fourier transforms. In addition, our method is treated as a universal plug-in, which can be incorporated into different models for performance improvement. In the end, we conduct extensive experiments to evaluate our method on the widely-used datasets including CUB-200–2011, CARS-196, and Stanford Online Products. Our quantitative results demonstrate that the proposed plug-in outperforms previous approaches consistently and significantly across different datasets and evaluation metrics.

Deep metric learning has become a key component of cross-modal retrieval. By learning to pull the features of matched instances closer while pushing the features of mismatched instances farther away, one can learn highly robust multi-modal representations. Most existing cross-modal retrieval methods leverage vanilla triplet loss to train the network, which cannot adaptively penalize pairs with different hardness. Although various weighting strategies have been designed for unimodal matching tasks, few weighting strategies have been applied to cross-modal tasks due to the specificity of cross-modal tasks. While few weighting strategies are designed for cross-modal scenarios, they usually involve a lot of hyper-parameters, which require a lot of computational resources to fine-tune. In this paper, we introduce a new exponential loss, which can assign appropriate weights to individual positive and negative pairs according to their similarity so that it can adaptively penalize pairs with different hardness. Furthermore, the exponential loss has only two hyper-parameters, making it easier to find the optimal parameters to suit various data distributions in practice. Exponential loss can be universally applied to well-established cross-modal models and further boost their retrieval performance. We exhaustively ablate our method on Image-Text matching, Video-Text matching, as well as unimodal Image matching. Experimental results show that a standard model trained with exponential loss can achieve noticeable performance gains.

Automatic detection and localization of objects in remote sensing images are of great significance for remote sensing systems. Existing frameworks usually train an object detection network using collected remote sensing images. However, these models usually perform poorly due to the lack of large-scale training datasets, which is often the case for special remote sensing scenarios, e.g., the detection of ships in the open sea. Although image synthesis is a common strategy to alleviate the issue of data insufficiency, the trained model still performs poorly when being tested on real-world scenes. Aimed at this, a novel sensor-related image synthesis framework, dubbed as remote sensing-image synthesis pipeline (RS-ISP), is developed to address the lack of on-orbit remote sensing images. Specifically, our RS-ISP introduces two novel designs to ensure the distribution consistency between the generated images and the real images: 1) the first is a novel pipeline for modeling the physical process of noise production during image capture using specific sensors and 2) the second is the design of a detection-oriented image harmonization model. Similar to the existing design, our model first produces coarse synthetic images by copy–paste operation, on which the proposed harmonization process is used to reduce the variation in the pasted foreground and background. By incorporating these two designs into a unified framework, our RS-ISP is designed and used to produce large-scale synthetic images used to train the object detection model for detecting ships in remote sensing images. Comparative experiments demonstrated that RS-ISP increased the [email protected] from 0.148 to 0.498 for the ship detection task. Code will be publicly available.

Cross-modal retrieval aims to retrieve one semantically similar media from multiple media types based on queries entered by another type of media. An intuitive idea is to map different media data into a common space and then directly measure content similarity between different types of data. In this paper, we present a novel method, called Category Alignment Adversarial Learning (CAAL) for cross-modal retrieval. It aims to find a common representation space supervised by category information, in which the samples from different modalities can be compared directly. Specifically, CAAL firstly employs two parallel encoders to generate common representations for image and text features respectively. Furthermore, we employ two parallel GANs with category information to generate fake image and text features which next will be utilized with already generated embedding to reconstruct the common representation. At last, two joint discriminators are utilized to reduce the gap between the mapping of the first stage and the embedding of the second stage. Comprehensive experimental results on four widely-used benchmark datasets demonstrate the superior performance of our proposed method compared with the state-of-the-art approaches.

The visual emotion recognition from facial expressions easily suffers barrier problems of varying brightness, head pose change, various image scales when the recognition is performed in different domains. Therefore, it is required to erase such domain barriers. Considering that the human expresses their emotions always relying on the muscle motion near five sense organs of face, local features around them are typically crucial. In this paper, we propose a Region Attention eNhanced Domain Adaptation (RANDA) approach for unsupervised cross-domain facial expression recognition (FER). In RANDA, we design an unsupervised domain adaptation solution that adopts an iterative pseudo label assignment method to provide pseudo labels in the target domain, then employs adversarial learning to confuse feature representation of facial expressions in the source and target domains. Furthermore, a facial landmark guided fine-grained region attention learning module is designed to enhance significant emotion features and simultaneously weaken domain discrepancy. The proposed RANDA is adopted for cross-domain emotion recognition, and extensive evaluations are performed on multiple datasets, i.e., CK+, MMI, SFEW, RAF-DB, AffectNet. Results indicate that the RANDA outperforms the state-of-the-art approaches. It provides an effective solution for the cross-domain FER.

Signed link prediction in social networks aims to reveal the underlying relationships (i.e., links) among users (i.e., nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task. Nevertheless, the existing graph-based approaches could hardly provide human-intelligible explanations for the following three questions: (1) which neighbors to aggregate, (2) which path to propagate along, and (3) which social theory to follow in the learning process. To answer the aforementioned questions, in this paper, we investigate how to reconcile thebalanceandstatussocial rules with information theory and develop a unified framework, termed as Signed Infomax Hyperbolic Graph (SIHG). By maximizing the mutual information between edge polarities and node embeddings, one can identify the most representative neighboring nodes that support the inference of edge sign. Different from existing GNNs that could only group features of friends in the subspace, the proposed SIHG incorporates the signed attention module, which is also capable of pushing hostile users far away from each other to preserve the geometry of antagonism. The polarity of the learned edge attention maps, in turn, provides interpretations of the social theories used in each aggregation. In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion. Extensive experiments on four signed network benchmarks demonstrate that the proposed SIHG framework significantly outperforms the state-of-the-arts in signed link prediction.

Rumors can cause devastating consequences to individuals and our society. Analysis shows that the widespread of rumors typically results from deliberate promotion of information with unknown veracity aiming to shape the collective public opinions on the concerned news event. In this paper, we attempt to combat such chaotic phenomenon with a countermeasure by mirroring against how such chaos is created in order to make automatic rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, further polarizing the original conversation threads with the intention of pressurizing the discriminator to learn stronger rumor indicative features from the augmented, more challenging examples. We reveal that feature learning effectiveness is highly relevant to the quality of generated parody, viz., how hard it is to get distinguished from real posts. Given the strong natural language generation performance of transformer, we propose a transformer-based method to improve the generated posts, so that they appear to be closely responsive to the source post and retain the authentic propagation structure and context of information. Different from traditional data-driven approach to rumor detection, our method can capture low-frequency but more salient non-trivial discriminant patterns via adversarial training. Extensive experiments on THREE benchmark datasets demonstrate that our rumor detection methods and the transformer-based model achieve much better results than state-of-the-art methods.

Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views with unclear clustering structures, resulting in poor multi-view clustering performance. To address this drawback, we proposeself-supervised discriminative feature learning fordeepmulti-viewclustering (SDMVC). Concretely, deep autoencoders are applied to learn embedded features for each view independently. To leverage the multi-view complementary information, we concatenate all views’ embedded features to form the global features, which can overcome the negative impact of some views’ unclear clustering structures. In a self-supervised manner, pseudo-labels are obtained to build a unified target distribution to perform multi-view discriminative feature learning. During this process, global discriminative information can be mined to supervise all views to learn more discriminative features, which in turn are used to update the target distribution. Besides, this unified target distribution can make SDMVC learn consistent cluster assignments, which accomplishes the clustering consistency of multiple views while preserving their features’ diversity. Experiments on various types of multi-view datasets show that SDMVC outperforms 14 competitors including classic and state-of-the-art methods. The code is available athttps://github.com/SubmissionsIn/SDMVC.

As an important visual understanding task, scene graph generation has been drawing widespread attention and could boost a broad range of downstream vision applications. Traditional scene graph generation methods based on different context refinements are trained with probabilistic chain rule, which treats objects and relationships as independent entities. Despite their surprisingly great progress, such a plain formulation unconsciously ignores the latent geometric structure of entities and relationships. To address this issue, we move beyond the traditional real-valued representations and useQuaternionRelationEmbedding (QuatRE) to generate scene graphs with more expressive hypercomplex representations. More specifically, we introduce the concept of quaternion representations, hyper-complex valued with three imaginary components for objects entities, then formulate the relation triplets with Hamilton product. Benefiting from explicitly modeling the latent inter-dependencies among all imaginary components and strong expressive capacity, our proposed QuatRE method could better capture the interactions between entities. More importantly, our novel QuatRE method can be treated as a plug-in and well generalized into other methods for performance improvement as it involves no additional layers. Finally, extensive comparisons of our proposed method against the state-of-the-art methods on two large-scale and widely-used datasets, i.e. Visual Genome and Open Images, demonstrated our superiority and generalization capability on various metrics for biased or unbiased inference.

In this paper, we study the composed query image retrieval, which aims at retrieving the target image similar to the composed query, i.e., a reference image and the desired modification text. Compared with conventional image retrieval, this task is more challenging as it not only requires precisely aligning the composed query and target image in a common embedding space, but also simultaneously extracting related information from the reference image and modification text. In order to properly extract related information from the composed query, existing methods usually embed vision-language inputs using different feature encoders, e.g., CNN for images and LSTM/BERT for text, and then employ a complicated manually-designed composition module for learning the joint image-text representation. However, the architecture discrepancy in feature encoders would restrict the vision-language plenitudinous interaction. Meanwhile, certain complicated composition designs might significantly hamper the generalization ability of the model. To tackle these problems, we propose a new framework termed ComqueryFormer, which effectively processes the composed query with the Transformer for this task. Specifically, to eliminate the architecture discrepancy, we leverage a unified transformer-based architecture to homogeneously encode the vision-language inputs. Meanwhile, instead of the complicated composition module, the neat yet effective cross-modal transformer is adopted to hierarchically fuse the composed query at various vision scales. On the other hand, we introduce an efficient global-local alignment module to narrow the distance between the composed query and the target image. It not only considers the divergence in the global joint embedding space but also forces the model to focus on the local detail differences. Extensive experiments on three real-world datasets demonstrate the superiority of our ComqueryFormer. Our code can be found at:https://github.com/uestc-xyh/ComqueryFormer.

The unpaired image-to-image translation aims to translate input images from one source domain to some desired outputs in a target domain by learning from unpaired training data. Cycle-consistency constraint provides a general principle to estimate and measure forward and backward mapping functions between two domains. In many cases, the information entropy of images from the two domains is not equal, resulting in an information-rich domain and an information-poor domain. However, existing solutions based on cycle-consistency either completely discard the information asymmetry between the two domains (a common choice), which leads to inferior translation performance for the asymmetric unpaired image-to-image translation, or have to rely on special task-specific designs and introduce extra loss components. These elaborative designs especially for the relatively harder translation direction from the information-poor domain to the information-rich domain (poor-to-rich translation) require extra labor and are limited to some specific tasks. In this paper, we propose a novel asynchronous generative adversarial network named Async-GAN, which provides a model-agnostic framework for easily turning symmetrical models into powerful asymmetric counterparts that can handle asymmetric unpaired image-to-image translation much better. The key innovation is to iteratively build gradually improving intermediate domains for generating pseudo paired training samples, which provide stronger full supervision for assisting the poor-to-rich translation. Extensive experiments on various asymmetric unpaired translation tasks demonstrate the superiority of the proposal. Furthermore, the proposed training framework could be extended to various Cycle-GAN solutions and achieve a performance gain.

Fine-grained visual classification (FGVC) is challenging due to the interclass similarity and intraclass variation in datasets. In this work, we explore the great merit of complex values in introducing an imaginary part for modeling data uncertainty (e.g., different points on the complex plane can describe the same state) and graph convolutional networks (GCNs) in learning interdependently among classes to simultaneously tackle the above two major challenges. To the end, we propose a novel approach, termed text-assisted complex-valued fusion network (TA-CFN). Specifically, we expand each feature from 1-D real values to 2-D complex value by disassembling feature maps, thereby enabling the extension of traditional deep convolutional neural networks over the complex domain. Then, we fuse the real and imaginary parts of complex features through complex projection and modulus operation. Finally, we build an undirected graph over the object labels with the assistance of a text corpus, and a GCN is learned to map this graph into a set of classifiers. The benefits are in two folds: 1) complex features allow for a richer algebraic structure to better model the large variation within the same category and 2) leveraging the interclass dependencies brought by the GCN to capture key factors of the slight variation among different categories. We conduct extensive experiments to verify that our proposed model can achieve the state-of-the-art performance on two widely used FGVC datasets.

Steganography is an important and prevailing information hiding tool to perform secret message transmission in an open environment. Existing steganography methods can mainly fall into two categories: predefined rule-based and data-driven methods. The former is susceptible to the statistical attack, while the latter adopts the deep convolution neural networks to promote security. However, deep learning-based methods suffer from perceptible artificial artifacts or deep steganalysis. In this article, we introduce a novel composition-aware image steganography (CAIS) to guarantee both visual security and resistance to deep steganalysis through the self-generated supervision. The key innovation is an adversarial composition estimation module, which has integrated the rule-based composition method and generative adversarial network to help synthesize steganographic images with more naturalness. We first perform a rule-based image blending method to obtain infinite synthetically data-label pairs. Then, we utilize an adversarial composition estimation branch to recognize the message feature pattern from the composite image based on these self-generated data-label pairs. Through the adversarial training, we force the steganography function to synthesize steganographic images, which can fool the composition estimation network. Thus, the proposed CAIS can achieve better information hiding and higher security to resist deep steganalysis. Furthermore, an effective global-and-part checking is designed to alleviate visual artifacts caused by hiding secret information. We conduct a comprehensive analysis of CAIS from various aspects (e.g., security and robustness) to verify the superior performance of the proposed method. Comprehensive experimental results on three large-scale widely used datasets have demonstrated the superior performance of our CAIS compared with several state-of-the-art approaches.

In this paper, we study the zero-shot sketch-based image retrieval (ZS-SBIR) task, which retrieves natural images related to sketch queries from unseen categories. In the literature, convolutional neural networks (CNNs) have become the de-facto standard and they are either trained end-to-end or used to extract pre-trained features for images and sketches. However, CNNs are limited in modeling the global structural information of objects due to the intrinsic locality of convolution operations. To this end, we propose a Transformer-based approach called Three-Way Vision Transformer (TVT) to leverage the ability of Vision Transformer (ViT) to model global contexts due to the global self-attention mechanism. Going beyond simply applying ViT to this task, we propose a token-based strategy of adding fusion and distillation tokens and making them complementary to each other. Specifically, we integrate three ViTs, which are pre-trained on data of each modality, into a three-way pipeline through the processes of distillation and multi-modal hypersphere learning. The distillation process is proposed to supervise fusion ViT (ViT with an extra fusion token) with soft targets from modality-specific ViTs, which prevents fusion ViT from catastrophic forgetting. Furthermore, our method learns a multi-modal hypersphere by performing inter- and intra-modal alignment without loss of uniformity, which aims to bridge the modal gap between modalities of sketch and image and avoid the collapse in dimensions. Extensive experiments on three benchmark datasets, i.e., Sketchy, TU-Berlin, and QuickDraw, demonstrate the superiority of our TVT method over the state-of-the-art ZS-SBIR methods.

We propose the Accelerated Sign Hunter (ASH), a sign-based black-box attack under l∞ constraint. The proposed method searches an approximate gradient sign of loss w.r.t. the input image with few queries to the target model and crafts the adversarial example by updating the input image in this direction. It applies a Branch-Prune Strategy that infers the unknown sign bits according to the checked ones to avoid unnecessary queries. It also adopts a Stabilized Hierarchical Search to achieve better performance within a limited query budget. We provide a theoretical proof showing that the Accelerated Sign Hunter halves the queries without dropping the attack success rate (SR) compared with the state-of-the-art sign-based black-box attack. Extensive experiments also demonstrate the superiority of our ASH method over other black-box attacks. In particular on Inception-v3 for ImageNet, our method achieves the SR of 0.989 with an average queries of 338.56, which is 1/4 fewer than that of the state-of-the-art sign-based attack to achieve the same SR. Moreover, our ASH method is out-of-the-box since there are no hyperparameters that need to be tuned.

Modelling the coherence of information is important for human to perceive and prehend the physical world. Existing works on coherence modelling mainly focus on single modality, which overlook the effect of information integration and semantic consistency across modalities. To fill the research gap, this paper targets at the cross-modal coherence modelling, specifically, the cross-modal ordering task. The task requires to not only explore the coherence information in single modality, but also leverage cross-modal information to model the semantic consistency between modalities. To this end, we propose a Non-Autoregressive Cross-modal Ordering Net (NACON) adopting a basic encoder-decoder architecture. Specifically, NACON is equipped with an order-invariant context encoder to model the unordered input set and a non-autoregressive decoder to generate ordered sequences in parallel. We devise a cross-modal positional attention module in NACON to take advantage of the cross-modal order guidance. To alleviate the repetition problem of non-autoregressive models, we introduce an elegant exclusive loss to constrain the ordering exclusiveness between positions and elements. We conduct extensive experiments on two assembled datasets to support our task, SIND and TACoS-Ordering. Experimental results show that the proposed NACON can effectively leverage cross-modal guidance and recover the correct order of the elements.The code is available at https://github.com/YiBin-CHN/CMCM.

View change brings a significant challenge to action representation and recognition due to pose occlusion and deformation. We propose a Global-Local Cross-View Fisher Discrimination (GL-CVFD) algorithm to tackle this problem. In the GL-CVFD approach, we firstly capture the motion trajectory of body joints in action sequences as feature input to weaken the effect of view change. Secondly, we design a Global-Local Cross-View Representation (CVR) learning module, which builds global-level and local-level graphs to link body parts and joints between different views. It can enhance the cross-view information interaction and obtain an effective view-common action representation. Thirdly, we present a Cross-View Fisher Discrimination (CVFD) module, which performs a view-differential operation to separate view-specific action features and modifies the Fisher discriminator to implement view-semantic Fisher contrastive learning. It operates by pulling and pushing on view-specific and view-common action features in the view term to guarantee the validity of the CVR module, then distinguishes view-common action features in the semantic term for view-invariant recognition. Extensive and fair evaluations are implemented in the UESTC, NTU 60, and NTU 120 datasets. Experiment results illustrate that our proposed approach achieves encouraging performance in skeleton-based view-invariant action recognition.

With the extensive application of deep learning, adversarial attacks especially query-based attacks receive more concern than ever before. However, the scenarios assumed by existing query-based attacks against image retrieval are usually too simple to satisfy the attack demand. In this paper, we propose a novel method termed Absolute-Relative Ranking Attack (ARRA) that considers a more practical attack scenario. Specifically, we propose two compatible goals for the query-based attack, i.e., absolute ranking attack and relative ranking attack, which aim to change the relative order of chosen candidates and assign the specific ranks to chosen candidates in retrieval list respectively. We further devise the Absolute Ranking Loss (ARL) and Relative Ranking Loss (RRL) for the above goals and implement our ARRA by minimizing their combination with black-box optimizers and evaluate the attack performance by attack success rate and normalized ranking correlation. Extensive experiments conducted on widely-used SOP and CUB-200 datasets demonstrate the superiority of the proposed approach over the baselines. Moreover, the attack result on a real-world image retrieval system, i.e., Huawei Cloud Image Search, also proves the practicability of our ARRA approach.

Existing object detection models have been demonstrated to successfully discriminate and localize the predefined object categories under the seen or similar situations. However, the open-world object detection as required by autonomous driving perception systems refers to recognizing unseen objects under various scenarios. On the one hand, the knowledge gap between seen and unseen object categories poses extreme challenges for models trained with supervision only from the seen object categories. On the other hand, the domain differences across different scenarios also cause an additional urge to take the domain gap into consideration by aligning the sample or label distribution. Aimed at resolving these two challenges simultaneously, we firstly design a pre-training model to formulate the mappings between visual images and semantic embeddings from the extra annotations as guidance to link the seen and unseen object categories through a self-supervised manner. Within this formulation, the domain adaptation is then utilized for extracting the domain-agnostic feature representations and alleviating the misdetection of unseen objects caused by the domain appearance changes. As a result, the more realistic and practical open-world object detection problem is visited and resolved by our novel formulation, which could detect the unseen categories from unseen domains without any bounding box annotations while there is no obvious performance drop in detecting the seen categories. We are the first to formulate a unified model for open-world task and establish a new state-of-the-art performance for this challenge.

The difficulty of image-text retrieval is further exacerbated by the phenomenon of one-to-many correspondence, where multiple semantic manifestations of the other modality could be obtained by a given query. However, the prevailing methods adopt the deterministic embedding strategy to retrieve the most similar candidate, which encodes the representations of different modalities as single points in vector space. We argue that such a deterministic point mapping is obviously insufficient to represent a potential set of retrieval results for one-to-many correspondence, despite its noticeable progress. As a remedy to this issue, we propose a Point to Rectangle Matching (abbreviated as P2RM) mechanism, which actually is a geometric representation learning method for image-text retrieval. Specifically, our intuitive insight is that the representations of different modalities could be extended to rectangles, then a set of points inside such a rectangle embedding could be semantically related to many candidate correspondences. Thus our P2RM method could essentially address the one-to-many correspondence. Besides, we design a novel semantic similarity measurement method from the perspective of distance for our rectangle embedding. Under the evaluation metric for multiple matches, extensive experiments and ablation studies on two commonly used benchmarks demonstrate our effectiveness and superiority in tackling the multiplicity of image-text retrieval.

Visual Question Answering (VQA) is a task to answer natural language questions tied to the content of visual images. Most recent VQA approaches usually apply attention mechanism to focus on the relevant visual objects and/or consider the relations between objects via off-the-shelf methods in visual relation reasoning. However, they still suffer from several drawbacks. First, they mostly model the simple relations between objects, which results in many complicated questions cannot be answered correctly, because of failing to provide sufficient knowledge. Second, they seldom leverage the harmony cooperation of visual appearance feature and relation feature. To solve these problems, we propose a novel end-to-end VQA model, termed Multi-modal Relation Attention Network (MRA-Net). The proposed model explores both textual and visual relations to improve performance and interpretability. In specific, we devise 1) a self-guided word relation attention scheme, which explore the latent semantic relations between words; 2) two question-adaptive visual relation attention modules that can extract not only the fine-grained and precise binary relations between objects but also the more sophisticated trinary relations. Both kinds of question-related visual relations provide more and deeper visual semantics, thereby improving the visual reasoning ability of question answering. Furthermore, the proposed model also combines appearance feature with relation feature to reconcile the two types of features effectively. Extensive experiments on five large benchmark datasets, VQA-1.0, VQA-2.0, COCO-QA, VQA-CP v2, and TDIUC, demonstrate that our proposed model outperforms state-of-the-art approaches.

Cross-modal retrieval has recently attracted growing attention, which aims to match instances captured from different modalities. The performance of cross-modal retrieval methods heavily relies on the capability of metric learning to mine and weight the informative pairs. While various metric learning methods have been developed for unimodal retrieval tasks, the cross-modal retrieval tasks, however, have not been explored to its fullest extent. In this paper, we develop a universal weighting metric learning framework for cross-modal retrieval, which can effectively sample informative pairs and assign proper weight values to them based on their similarity scores so that different pairs favor different penalty strength. Based on this framework, we introduce two types of polynomial loss for cross-modal retrieval, self-similarity polynomial loss and relative-similarity polynomial loss. The former provides a polynomial function to associate the weight values with self-similarity scores, and the latter defines a polynomial function to associate the weight values with relative-similarity scores. Both self and relative-similarity polynomial loss can be freely applied to off-the-shelf methods and further improve their retrieval performance. Extensive experiments on two image-text retrieval datasets, three video-text retrieval datasets and one fine-grained image retrieval dataset demonstrate that our proposed method can achieve a noticeable boost in retrieval performance.

Recently, generative adversarial network (GAN) has shown its strong ability on modeling data distribution via adversarial learning. Cross-modal GAN, which attempts to utilize the power of GAN to model the cross-modal joint distribution and to learn compatible cross-modal features, is becoming the research hotspot. However, the existing cross-modal GAN approaches typically 1) require labeled multimodal data of massive labor cost to establish cross-modal correlation; 2) utilize the vanilla GAN model that results in unstable training procedure and meaningless synthetic features; and 3) lack of extensibility for retrieving cross-modal data of new classes. In this article, we revisit the adversarial learning in existing cross-modal GAN methods and propose Joint Feature Synthesis and Embedding (JFSE), a novel method that jointly performs multimodal feature synthesis and common embedding space learning to overcome the above three shortcomings. Specifically, JFSE deploys two coupled conditional Wassertein GAN modules for the input data of two modalities, to synthesize meaningful and correlated multimodal features under the guidance of the word embeddings of class labels. Moreover, three advanced distribution alignment schemes with advanced cycle-consistency constraints are proposed to preserve the semantic compatibility and enable the knowledge transfer in the common embedding space for both the true and synthetic cross-modal features. All these add-ons in JFSE not only help to learn more effective common embedding space that effectively captures the cross-modal correlation but also facilitate to transfer knowledge to multimodal data of new classes. Extensive experiments are conducted on four widely used cross-modal datasets, and the comparisons with more than ten state-of-the-art approaches show that our JFSE method achieves remarkably accuracy improvement on both standard retrieval and the newly explored zero-shot and generalized zero-shot retrieval tasks.

Embeddings of textual data containing location names (e.g., social media posts) have essential applications in various contexts such as marketing and disaster management. In these downstream implementations, social biases behind location names are highly prone to introduce unfair results through their embeddings; for example, emergent text messages with swapped location names might result in varied rescue responses. Hence, it is critical to address social biases encoded in location names and to seek its mitigation. Prevalent works addressing biases in embeddings mainly focus on individual attributes like gender or ethnicity. Yet, a large number of social attributes behind location names (e.g., income level and population density) makes it challenging to originate the source of biases. Existing mitigation methods based on finding attribute subspaces cannot be simply applied to address social biases. Moreover, bias mitigation tends to simultaneously remove necessary semantics from embeddings, making it difficult to achieve a balance between mitigation performance and semantics retention. In this article, we first employ the concept of counterfactual fairness to investigate the social biases encoded in training data. Then, we quantify the biases in the contextual embeddings (BERT and ELMo). We report a high correlation between biases in the training data and embeddings. Next, we introduce a novel bias mitigation algorithm that customizes bias representations for any location names. The method yields debiased location name vectors for various social attributes simultaneously. The proposed algorithm achieves a better mitigation performance on overall attributes compared with a prevalent postprocessing method, while maintaining correctness by retaining semantic information.

To explore the specific visual aspects and the language consistency at the same time, this paper introduces a new image captioning task, dubbedentity slot filling captioning (ESFCap). It is similar to the masked entity completion tasks in NLP, which are widely used to study language context and has been successfully employed to improve language understanding. Specifically, given a sentence with blank for describing an image, the ESFCap task aims to fill the blank with proper text content according to the visual information. The filled text should be grounded to correct visual entities and also in concordance with the sentence structure. To support the ESFCap research, we collect and release an entity slot filling captioning dataset,Flickr30k-EnFi, based on Flickr30k-Entities. The Flickr30k-EnFi dataset consists of 31,783 images and 565,750 masked sentences, as well as the text snippets for the masked slot. For tackling the ESFCap task, we propose a multi-modal fusion model equipped with a novel adaptive dynamic attention module, termed AdaMFN. The AdaMFN model effectively leverages both global and local information from vision and language. It is also able to adaptively focus on the key linguistic knowledge and visual regions to generate correct filling results. The experimental results and analysis demonstrate the effectiveness of our proposed model.

Visual sound separation (VSS) aims to obtain each sound component from the mixed audio signals with the guidance of visual information. Existing works mainly capture the global-level audio-visual correspondence and exploit various visual features to enhance the appearance and motion features of visual modality. However, they commonly neglect the intrinsic properties of the audio modality, resulting in less effective audio feature extraction and unbalanced audio-visual correspondence. To tackle this problem, we propose a novel end-to-end framework termed Modeling Two-Stream Correspondence (MTSC) for VSS by explicitly extracting the timbre and content features in audio modality. The proposed MTSC method employs a two-stream architecture to enhance audio-visual correspondence for both the appearance-timbre and motion-content features. Moreover, with the advanced two-stream pipeline, more lightweight appearance and motion features for visual modality are exploited. Extensive experiments conducted on two benchmark musical instrument datasets demonstrate that with the above properties, our MTSC method remarkably outperforms seven state-of-the-art VSS approaches. The implementation code and extensive experimental results of the proposed MTSC method are provided athttps://github.com/CFM-MSG/MTSC-VSS.

Visual Question Answering (VQA) is a very challenging task, which requires to understand visual images and natural language questions simultaneously. In the open-ended VQA task, most previous solutions focus on understanding the question and image contents, as well as their correlations. However, they mostly reason the answers in a one-stage way, which results in that the generated answers are significantly ignored. In this paper, we propose a novel approach, termed Cascaded-Answering Model (CAM), which extends the conventional one-stage VQA model to a two-stage model. Hence, the proposed model can fully explore the semantics embedded in the predicted answers. Specifically, CAM is composed of two cascaded answering modules: Candidate Answer Generation (CAG) module and Final Answer Prediction (FAP) module. In CAG module, we select multiple relevant candidates from the generated answers using a typical VQA approach with Co-Attention. While in FAP module, we integrate the information of question and image, together with the semantics explored from the selected candidate answers to predict the final answer. Experimental results demonstrate that the proposed model produces high-quality candidate answers and achieves the state-of-the-art performance on five large benchmark datasets, VQA-1.0, VQA-2.0, VQA-CP v2, TDIUC and COCO-QA.

The majority of current research on sensor network localization focuses on wireless sensor networks deployed on two-dimensional (2D) plane or in three-dimensional (3D) space, very few on the 3D terrain surface. However, many real-world applications require large-scale sensor networks deployed on the surface of a complex 3D terrain. Compared with planar and 3D network localization, terrain surface network localization generates unique and fundamental hardness. We explore 3D surface network localization with terrain models. A digital terrain model (DTM), available to the public with a variable resolution of up to one meter, is a 3D representation of a terrain's surface. It is commonly built using remote sensing technology or from land surveying and can be easily converted to a triangular mesh. Given a sensor network deployed on the surface of a 3D terrain with one-hop distance information available, we can extract a triangular mesh from the connectivity graph of the network. The constraint that the sensors must be on the known 3D terrain's surface ensures that the triangular meshes of the network and the terrain's surface overlap and approximate the same geometric shape. The basic idea of the localization algorithms is to map the two triangular meshes extracted from the connectivity graph of a sensor network and the DTM of its deployed terrain surface to the plane. The two meshes mapped to the plane can be easily aligned if the location information of anchor nodes is available. We introduce a fully distributed algorithm to construct a well-aligned mapping between the two triangular meshes in the plane based on anchor nodes information. However, accidents may happen on anchor nodes. We then introduce an anchor-free algorithm to extract feature points with geometric properties intrinsic to surface distances and independent of the embedding of the two meshes in 3D. The matched feature points induce transformations to align the two meshes in the plane. With the aligned triangular meshes of a network and its deployed terrain surface, each sensor node of the network can easily locate reference grid points from the DTM of the terrain to calculate its own geographic location. We carry out extensive simulations under various scenarios to evaluate the overall performance of the proposed algorithms with different factors such as the one-hop distance measurement error, the resolution of a DTM, and the performance of the algorithm in the situation of connectivity only.

Targeted attack aims to mislead the classification model to a specific class, and it can be further divided into black-box and white-box targeted attack depending on whether the classification model is known. A growing number of approaches rely on disrupting the image representations to craft adversarial examples. However, this type of methods often suffer from either lowwhite-box targetedattack success rate or poorblack-box targetedattack transferability. To address these problems, we propose a Transferable Attentive Attack (TAA) method which adds perturbation to clean images based on the attended regions and features. This is motivated by one important observation that deep-learning based classification models (or even shallow-learning based models like SIFT) make the prediction mainly based on the informative and discriminative regions of an image. Specifically, the corresponding features of the informative regions are firstly extracted, and theanchorimage’s features are iteratively “pushed” away from the source class and simultaneously “pulled” closer to the target class along with attacking. Moreover, we introduce a new strategy that the attack selects the centroids of source and target class cluster as the input of triplet loss to achieve high transferability. Experimental results demonstrate that our method improves the transferability of adversarial example, while maintaining higher success rate forwhite-box targetedattacks compared with the state-of-the-arts. In particular, TAA attacks on image-representation based task like VQA also result in a significant performance drop in terms of accuracy.

It is well known that humans can learn and recognize objects effectively from several limited image samples. However, learning from just a few images is still a tremendous challenge for existing main-stream deep neural networks. Inspired by analogical reasoning in the human mind, a feasible strategy is to “translate” the abundant images of a rich source domain to enrich the relevant yet different target domain with insufficient image data. To achieve this goal, we propose a novel, effective multi-adversarial framework (MA) based on part-global learning, which accomplishes the one-shot cross-domain image-to-image translation. In specific, we first devise a part-global adversarial training scheme to provide an efficient way for feature extraction and prevent discriminators from being overfitted. Then, a multi-adversarial mechanism is employed to enhance the image-to-image translation ability to unearth the high-level semantic representation. Moreover, a balanced adversarial loss function is presented, which aims to balance the training data and stabilize the training process. Extensive experiments demonstrate that the proposed approach can obtain impressive results on various datasets between two extremely imbalanced image domains and outperform state-of-the-art methods on one-shot image-to-image translation. Our code will be released with this paper athttps://github.com/zhengziqiang/OST.

Recurrent neural networks (RNNs) have gained tremendous popularity in almost every sequence modeling task. Despite the effort, these kinds of discrete unstructured data, such as texts, audio, and videos, are still difficult to be embedded in the feature space. Studies in improving the neural networks have accelerated since the introduction of more complex or deeper architectures. The improvements of previous methods are highly dependent on the model at the expense of huge computational sources. However, few of them pay attention to the algorithm. In this article, we bridge the Taylor series with the construction of RNN. Training RNN can be considered as a parameter estimate for the Taylor series. However, we found that there is a discrete term called the remainder in the finite Taylor series that cannot be optimized using gradient descent, which is part of the reason for the truncation error and the model falling into the local optimal solution. To address this, we propose a training algorithm that estimates the range of remainder and introduces the remainder obtained by sampling in this continuous space into the RNN to assist in optimizing the parameters. Notably, the performance of RNN can be improved without changing the RNN architecture in the testing phase. We demonstrate that our approach is able to achieve state-of-the-art performance in action recognition and cross-modal retrieval tasks.

Artificial intelligence including deep learning and 3D reconstruction methods is changing the daily life of people. Now, an unmanned aerial vehicle that can move freely in the air and avoid harsh ground conditions has been commonly adopted as a suitable tool for 3D reconstruction. The traditional 3D reconstruction mission based on drones usually consists of two steps: image collection and offline post-processing. But there are two problems: one is the uncertainty of whether all parts of the target object are covered, and another is the tedious post-processing time. Inspired by modern deep learning methods, we build a telexistence drone system with an onboard deep learning computation module and a wireless data transmission module that perform incremental real-time dense reconstruction of urban cities by itself. Two technical contributions are proposed to solve the preceding issues. First, based on the popular depth fusion surface reconstruction framework, we combine it with a visual-inertial odometry estimator that integrates the inertial measurement unit and allows for robust camera tracking as well as high-accuracy online 3D scan. Second, the capability of real-time 3D reconstruction enables a new rendering technique that can visualize the reconstructed geometry of the target as navigation guidance in the HMD. Therefore, it turns the traditional path-planning-based modeling process into an interactive one, leading to a higher level of scan completeness. The experiments in the simulation system and our real prototype demonstrate an improved quality of the 3D model using our artificial intelligence leveraged drone system.

Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain. Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making them unbalanced OSDA methods. To alleviate this problem, we propose a balanced OSDA methods which could recognize the unknown samples while maintain high classification performance for the known samples. Specifically, to reduce the domain gaps, we first project the features to a hyperspherical latent space. In this space, we propose to bound the centroid deviation angles to not only increase the intra-class compactness but also enlarge the inter-class margins. With the bounded centroid deviation angles, we employ the statistical Extreme Value Theory to recognize the unknown samples that are misclassified into known classes. In addition, to learn better centroids, we propose an improved centroid update strategy based on sample reweighting and adaptive update rate to cooperate with centroid alignment. Experimental results on three OSDA benchmarks verify that our method can significantly outperform the compared methods and reduce the proportion of the unknown samples being misclassified into known classes.

In many real-world applications, the amount of data available for training is often limited, and thus inductive bias and auxiliary knowledge are much needed for regularizing model training. One popular regularization method is to impose prior distribution assumptions on model parameters, and many recent works also attempt to regularize training by integrating external knowledge into specific neurons. However, existing regularization methods did not take account of the interaction between connected neuron pairs, which is invaluable internal knowledge for adaptive regularization for better representation learning as training progresses. In this paper, we explicitly take into account the interaction between connected neurons, and propose an adaptive internal knowledge driven regularization method, CORR-Reg. The key idea of CORR-Reg is to give a higher significance weight to connections of more correlated neuron pairs. The significance weights adaptively identify more important input neurons for each neuron. Instead of regularizing connection model parameters with a static strength such as weight decay, CORR-Reg imposes weaker regularization strength on more significant connections. As a consequence, neurons attend to more informative input features and thus learn more diversified and discriminative representation. We derive CORR-Reg with Bayesian inference framework and propose a novel optimization algorithm with Lagrange multiplier method and Stochastic Gradient Descent. Extensive evaluations on diverse benchmark datasets and neural network structures show that CORR-Reg achieves significant improvement over state-of-the-art regularization methods.

Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes. Previous methods focused on learning direct embeddings from global features to the semantic space in hope of knowledge transfer from seen classes to unseen classes. However, an unseen class shares local visual features with a set of seen classes and leveraging global visual features makes the knowledge transfer ineffective. To tackle this problem, we propose a Region Semantically Aligned Network (RSAN), which maps local features of unseen classes to their semantic attributes. Instead of using global features which are obtained by an average pooling layer after an image encoder, we directly utilize the output of the image encoder which maintains local information of the image. Concretely, we obtain each attribute from a specific region of the output and exploit these attributes for recognition. As a result, the knowledge of seen classes can be successfully transferred to unseen classes in a region-bases manner. In addition, we regularize the image encoder through attribute regression with a semantic knowledge to extract robust and attribute-related visual features. Experiments on several standard ZSL datasets reveal the benefit of the proposed RSAN method, outperforming state-of-the-art methods.

Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD) framework to improve the generalization performance of dehazing. Starting from a dehazing model backbone that is pre-trained on synthetic data, PSD exploits real hazy images to fine-tune the model in an unsupervised fashion. For the fine-tuning, we leverage several well-grounded physical priors and combine them into a prior loss committee. PSD allows for most of the existing dehazing models as its backbone, and the combination of multiple physical priors boosts dehazing significantly. Through extensive experiments, we demonstrate that our PSD framework establishes the new state-of-the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

We address the problem of localizing a specific moment from an untrimmed video by a language sentence query. Generally, previous methods mainly exist two problems that are not fully solved: 1) How to effectively model the fine-grained visual-language alignment between video and language query? 2) How to accurately localize the moment in the original video length? In this paper, we streamline the temporal language localization as a novel multi-stage aggregated transformer network. Specifically, we first intro-duce a new visual-language transformer backbone, which enables iterations and alignments among all elements in visual and language sequences. Different from previous multi-modal transformers, our backbone keeps both structure unified and modality specific. Moreover, we also pro-pose a multi-stage aggregation module topped on the trans-former backbone. In this module, we compute three stage-specific representations corresponding to different moment stages respectively, i.e. starting, middle and ending stages, for each video element. Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation. Because the obtained moment representation captures the stage specific information, it is very discriminative for accurate localization. Extensive experiments on ActivityNet Captions and TACoS datasets demonstrate our proposed method achieves significant improvements compared with all other methods.

In the field of video-text matching, there are several potential and effective internal relations within a single modal data, which the existing approaches always ignore. In this paper, we propose a novel model named Attention-based Relation Reasoning Network (ARRN), that can robustly learn and reason the word relations of a sentence and temporal relations between video frames. It can jointly capture the local and global characteristics of video and text, thus significantly improves the performance on video-text retrieval. In ARRN, with global-to-local attention strategy, we could attend to important relations of multi-scales, then learn more reasonable local relation features. These features, generated at distinct levels, are powerful and complementary to each other, allowing us to obtain effective video and text representations by very simple fusion. The extensive experiments on two widely-used video-text datasets MSVD and TGIF show that our proposed ARRN approach establishes a substantial improvement.

In recent years, as the fundamental task in video understanding, Temporal Action Detection is attracting extensive attention. Most existing approaches use the same model parameters to process all input videos, which are not adaptive to the input video during the inference stage. In this paper, we propose a novel model termed Multi-scale Dynamic Network (MDN) to tackle this problem. The proposed MDN model incorporates multiple Multi-scale Dynamic Modules (MDMs). Each MDM can generate video-specific and segment-specific convolution kernels based on video content from different scales and adaptively capture rich semantic information for the prediction. Besides, we also design a new Edge Suppression Loss (ESL) function for MDN to pay more attention to hard examples. Extensive experiments conducted on two popular benchmarks ActivityNet-1.3 and THUMOS-14 show that the proposed MDN model achieves the state-of-the-art performance.

The goal of Zero-shot Learning (ZSL) is to recognize categories that are not seen during the training process. The traditional method is to learn an embedding space and map visual features and semantic features to this common space. However, this method inevitably encounters the bias problem, i.e., unseen instances are often incorrectly recognized as the seen classes. Some attempts are made by proposing another paradigm, which uses generative models to hallucinate the features of unseen samples. However, the generative models often suffer from instability issues, making it impractical for them to generate fine-grained features of unseen samples, thus resulting in very limited improvement. To resolve this, a Semantic Enhanced Cross-modal GAN (SECM GAN) is proposed by imposing the cross-modal association for improving the semantic and discriminative property of the generated features. Specifically, we first train a cross-modal embedding model called Semantic Enhanced Cross-modal Model (SECM), which is constrained by discrimination and semantics. Then we train our generative model based on Generative Adversarial Network (GAN) called SECM GAN, in which the generator generates cross-modal features, and the discriminator distinguishes true cross-modal features from generated cross-modal features. We deploy SECM as a weak constraint of GAN, which makes reliance on GAN get reduced. We evaluate extensive experiments on three widely used ZSL datasets to demonstrate the superiority of our framework.

Composed query image retrieval is a growing research topic. The object is to retrieve images not only generally resemble the reference image, but differ according to the desired modification text. Existing methods mainly explore composing modification text with global feature or local entity descriptor of reference image. However, they ignore the fact that modification text is indeed diverse and arbitrary. It not only relates to abstractive global feature or concrete local entity transformation, but also often associates with the fine-grained structured visual adjustment. Thus, it is insufficient to emphasize the global or local entity visual for the query composition. In this work, we tackle this task by hierarchical composition learning. Specifically, the proposed method first encodes images into three representations consisting of global, entity and structure level representations. Structure level representation is richly explicable, which explicitly describes entities as well as attributes and relationships in the image with a directed graph. Based on these, we naturally perform hierarchical composition learning by fusing modification text and reference image in the global-entity-structure manner. It can transform the visual feature conditioned on modification text to target image in a coarse-to-fine manner, which takes advantage of the complementary information among three levels. Moreover, we introduce a hybrid space matching to explore global, entity and structure alignments which can get high performance and good interpretability.

A new form of telexistence is achieved by recording videos with a camera on an Uncrewed aerial vehicle (UAV) and playing the videos to a user via a head-mounted display (HMD). One key problem here is how to let the user freely and naturally control the UAV and thus the viewpoint. In this paper, we develop an HMD-based telexistence technique that achieves full 6- DOF control of the viewpoint. The core of our technique is an improved rate-based control technique with our adaptive origin update (AOU), in which the origin of the coordinate system of the user changes adaptively. This makes the user naturally perceive the origin and thus easily perform the control motion to get his/her desired viewpoint changing. As a consequence, without the aid of any auxiliary equipment, the AOU scheme handles the well known self-centering problem in the rate-based control methods. A real prototype is also built to evaluate this feature of our technique. To explore the advantage of our telexistence technique, we further use it as an interactive tool to perform the task of 3D scene reconstruction. User studies demonstrate that comparing with other telexistence solutions and the widely used joystick-based solutions, our solution largely reduces the workload and saves time and moving distance for the user.

Intrusion detection is an important technique that can provide solid protection for the network equipment against the security attacks. However, the attacks are usually unbalanced in different types and the attacks of unknown classes may also occur with the growth of Internet construction. In this case, the traditional machine learning-based intrusion detection methods usually have inferior detection accuracy and high false-positive rates. To tackle this problem, in this article, we propose a novel deep learning-based intrusion detection method named log-cosh conditional variational autoencoder (LCVAE). It inherits the capability of the conditional variational autoencoder (CVAE) that can capture the complex distribution of observed data and generate new data with prespecified classes. Different from the traditional CVAE, to better model the discrete property in the intrusion data, we design an effective loss term using the log hyperbolic cosine (log-cosh) function in the proposed LCVAE method. It can well balance the generation and reconstruction procedures and is more effective to generate diverse intrusion data for the imbalanced classes. To improve the detection accuracy, we utilize the classification based on convolutional neural network to perform feature extraction and classification based on the observed and generated intrusion data. We conduct extensive experiments on the challenging data set NSL-KDD with large-scale intrusion data. The results show that the superior detection performance of the proposed LCVAE method comparing with several state-of-the-art intrusion detection methods, and also demonstrate the potentiality of generating new intrusion data with promising diversity.

Current researches of action recognition which focus on single-view and multi-view recognition can hardly satisfy the requirements of human-robot interaction (HRI) applications for recognizing human actions from arbitrary views. Arbitrary-view recognition is still a challenging issue due to view changes and visual occlusions. In addition, the lack of datasets also sets up barriers. To provide data for arbitrary-view action recognition, we collect a new large-scale RGB-D action dataset for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The dataset includes action samples captured in 8 fixed viewpoints and varying-view sequences which cover the entire 360° view angles. In total, 118 persons are invited to act 40 action categories. Our dataset involves more participants, more viewpoints and a large number of samples. More importantly, it is the first dataset containing the entire 360° varying-view sequences. The dataset provides sufficient data for multi-view, cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance, and our dataset provides valuable but challenging data for the evaluation of arbitrary-view recognition.

Domain adaptation is suitable for transferring knowledge learned from one domain to a different but related domain. Considering the substantially large domain discrepancies, learning a more generalized feature representation is crucial for domain adaptation. On account of this, we propose an adaptive component embedding (ACE) method, for domain adaptation. Specifically, ACE learns adaptive components across domains to embed data into a shared domain-invariant subspace, in which the first-order statistics is aligned and the geometric properties are preserved simultaneously. Furthermore, the second-order statistics of domain distributions is also aligned to further mitigate domain shifts. Then, the aligned feature representation is classified by optimizing the structural risk functional in the reproducing kernel Hilbert space (RKHS). Extensive experiments show that our method can work well on six domain adaptation benchmarks, which verifies the effectiveness of ACE.

Understanding the surrounding environment is crucial for autonomous vehicles to make correct driving decisions. In particular, urban scene segmentation is a significant integral module commonly equipped in the perception system of autonomous vehicles to understand the real scene like a human. Any missegmentation of the driving scenario can potentially result in uncontrollable consequences such as serious accidents or the exception of the perception system. In this article, we investigate the vulnerability of the popular scene segmentation models designed with the backbones of deep neural networks (DNNs), which have been shown to be sensitive to adversarial attacks. Specifically, we propose an iterative projected gradient-based attack method that can effectively fool several DNN-based segmentation models with a remarkably higher attacking successful rate, and much smaller adversarial perturbations. Moreover, we also develop an adversarial training algorithm with min-max optimization style to enrich the robustness of the scene segmentation models. Extensive experiments on the Cityscape benchmark dataset consisting of large-scale urban scene images for autonomous vehicles demonstrate the effectiveness of our proposed attack method, as well as the benefit of the adversarial training scheme for the scene segmentation models.

Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly guarantee a satisfactory performance with the extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss and the shrink of the discrete solution space. In this article, we propose a novelCollaborative Learningstrategy that is tailored for generating high-quality low-bit hash codes. The core idea is to jointly distill bit-specific and informative representations for a group of pre-defined code lengths. The learning of short hash codes among the group can benefit from the manifold shared with other long codes, where multiple views from different hash codes provide the supplementary guidance and regularization, making the convergence faster and more stable. To achieve that, an asymmetric hashing framework with two variants of multi-head embedding structures is derived, termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of training and querying. Extensive experiments on three benchmark datasets have been conducted to verify the superiority of the proposed MAH, and have shown that the 8-bit hash codes generated by MAH achieve 94.3 percent of the MAP11.Mean Average Precision (MAP)score on the CIFAR-10 dataset, which significantly surpasses the performance of the 48-bit codes by the state-of-the-arts in image retrieval tasks.

Hashing methods have been extensively applied to efficient multimedia data indexing and retrieval on account of the explosion of multimedia data. Cross-modal hashing usually learns binary codes by mapping multi-modal data into a common Hamming space. Most supervised methods utilize relation information like class labels as pairwise similarities of cross-modal data pair to narrow intra-modal and inter-modal gap. In this paper, we propose a novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modal Hashing (SRLCH), which exploits relation information of labels in semantic space to make similar data from different modalities closer in the low-dimension Hamming subspace. SRLCH preserves the modality relationships, the discrete constraints and nonlinear structures, while admitting a closed-form binary codes solution, which effectively enhances the training efficiency. An iterative alternative optimization algorithm is developed to simultaneously learn both hash functions and unified binary codes. With these binary codes and hash functions, we can index multimedia data and search them in an efficient way. Evaluations in two cross-modal retrieval tasks on several widely-used datasets show that the proposed SRLCH outperforms most cross-modal hashing methods. Theoretical analysis also illustrates reasons for our method’s promotion in subspace relation learning.

In this article, we address the problem of visual question generation (VQG), a challenge in which a computer is required to generate meaningful questions about an image targeting a given answer. The existing approaches typically treat the VQG task as a reversed visual question answer (VQA) task, requiring the exhaustive match among all the image regions and the given answer. To reduce the complexity, we propose an innovative answer-centric approach termed radial graph convolutional network (Radial-GCN) to focus on the relevant image regions only. Our Radial-GCN method can quickly find the core answer area in an image by matching the latent answer with the semantic labels learned from all image regions. Then, a novel sparse graph of the radial structure is naturally built to capture the associations between the core node (i.e., answer area) and peripheral nodes (i.e., other areas); the graphic attention is subsequently adopted to steer the convolutional propagation toward potentially more relevant nodes for final question generation. Extensive experiments on three benchmark data sets show the superiority of our approach compared with the reference methods. Even in the unexplored challenging zero-shot VQA task, the synthesized questions by our method remarkably boost the performance of several state-of-the-art VQA methods from 0% to over 40%. The implementation code of our proposed method and the successfully generated questions are available at https://github.com/Wangt-CN/VQG-GCN.

Image-sentence matching is a challenging task in the field of language and vision, which aims at measuring the similarities between images and sentence descriptions. Most existing methods independently map the global features of images and sentences into a common space to calculate the image-sentence similarity. However, the image-sentence similarity obtained by these methods may be coarse as (1) an intermediate common space is introduced to implicitly match the heterogeneous features of images and sentences in a global level, and (2) only the inter-modality relations of images and sentences are captured while the intra-modality relations are ignored. To overcome the limitations, we propose a novel Cross-Modal Hybrid Feature Fusion (CMHF) framework for directly learning the image-sentence similarity by fusing multimodal features with inter- and intra-modality relations incorporated. It can robustly capture the high-level interactions between visual regions in images and words in sentences, where flexible attention mechanisms are utilized to generate effective attention flows within and across the modalities of images and sentences. A structured objective with ranking loss constraint is formed in CMHF to learn the image-sentence similarity based on the fused fine-grained features of different modalities bypassing the usage of intermediate common space. Extensive experiments and comprehensive analysis performed on two widely used datasets—Microsoft COCO and Flickr30K—show the effectiveness of the hybrid feature fusion framework in CMHF, in which the state-of-the-art matching performance is achieved by our proposed CMHF method.

Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data. However, most mainstream models suffer from catastrophic forgetting and insufficient robustness issues, thereby failing to fully retain or exploit long-term knowledge while being prone to cause severe error accumulation. In this paper, we propose a novel Continual Meta-Learning approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically formulates meta-learning as continual learning of a sequence of tasks. With each task forming as a graph, the intra- and inter-task correlations can be well preserved via message-passing and history transition. To remedy topological uncertainty from graph initialization, we utilize Bayes by Backprop strategy that approximates the posterior distribution of task-specific parameters with amortized inference networks, which are seamlessly integrated into the end-to-end edge learning. Extensive experiments conducted on the miniImageNet and tieredImageNet datasets demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 42.8% compared with state-of-the-art on the miniImageNet 5-way 1-shot classification task.

Domain adaptive image retrieval includes single-domain retrieval and cross-domain retrieval. Most of the existing image retrieval methods only focus on single-domain retrieval, which assumes that the distributions of retrieval databases and queries are similar. However, in practical application, the discrepancies between retrieval databases often taken in ideal illumination/pose/background/camera conditions and queries usually obtained in uncontrolled conditions are very large. In this paper, considering the practical application, we focus on challenging cross-domain retrieval. To address the problem, we propose an effective method named Probability Weighted Compact Feature Learning (PWCF), which provides inter-domain correlation guidance to promote cross-domain retrieval accuracy and learns a series of compact binary codes to improve the retrieval speed. First, we derive our loss function through the Maximum A Posteriori Estimation (MAP): Bayesian Perspective (BP) induced focal-triplet loss, BP induced quantization loss and BP induced classification loss. Second, we propose a common manifold structure between domains to explore the potential correlation across domains. Considering the original feature representation is biased due to the inter-domain discrepancy, the manifold structure is difficult to be constructed. Therefore, we propose a new feature named Histogram Feature of Neighbors (HFON) from the sample statistics perspective. Extensive experiments on various benchmark databases validate that our method outperforms many state-of-the-art image retrieval methods for domain adaptive image retrieval. The source code is available at {https://github.com/fuxianghuang1/PWCF}.

Cross-modal matching has been a highlighted research topic in both vision and language areas. Learning appropriate mining strategy to sample and weight informative pairs is crucial for the cross-modal matching performance. However, most existing metric learning methods are developed for unimodal matching, which is unsuitable for cross-modal matching on multimodal data with heterogeneous features. To address this problem, we propose a simple and interpretable universal weighting framework for cross-modal matching, which provides a tool to analyze the interpretability of various loss functions. Furthermore, we introduce a new polynomial loss under the universal weighting framework, which defines a weight function for the positive and negative informative pairs respectively. Experimental results on two image-text matching benchmarks and two video-text matching benchmarks validate the efficacy of the proposed method.

Adversarial attacks are very successful on image classification, but there are few researches on vision-language systems, such as image captioning. In this paper, we study the robustness of a CNN+RNN based image captioning system being subjected to adversarial noises in complex domain. In particular, we propose Fooled-by-Imagination, a novel algorithm for crafting adversarial examples with semantic embedding of targeted caption as perturbation in complex domain. The proposed algorithm explores the great merit of complex values in introducing imaginary part for modeling adversarial perturbation, and maintains the similarity of the image in real part. Our approach provides two evaluation approaches, which check whether neural image captioning systems can be fooled to output some randomly chosen captions or keywords. Besides, our method has good transferability under black-box setting. At last, our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords at a higher success rate.

Learning the relationship between the multi-modal data, e.g., texts, images and videos, is a classic task in the multimedia community. Cross-modal retrieval (CMR) is a typical example where the query and the corresponding results are in different modalities. Yet, a majority of existing works investigate CMR with an ideal assumption that the training samples in every modality are sufficient and complete. In real-world applications, however, this assumption does not always hold. Mismatch is common in multi-modal datasets. There is a high chance that samples in some modalities are either missing or corrupted. As a result, incomplete CMR has become a challenging issue. In this paper, we propose a Dual-Aligned Variational Autoencoders (DAVAE) to address the incomplete CMR problem. Specifically, we propose to learn modality-invariant representations for different modalities and use the learned representations for retrieval. We train multiple autoencoders, one for each modality, to learn the latent factors among different modalities. These latent representations are further dual-aligned at the distribution level and the semantic level to alleviate the modality gaps and enhance the discriminability of representations. For missing instances, we leverage generative models to synthesize latent representations for them. Notably, we test our method with different ratios of random incompleteness.Extensive experiments on three datasets verify that our method can consistently outperform the state-of-the-arts.

Recently, feature generating methods have been successfully applied to zero-shot learning (ZSL). However, most previous approaches only generate visual representations for zero-shot recognition. In fact, typical ZSL is a classic multi-modal learning protocol which consists of a visual space and a semantic space. In this paper, therefore, we present a new method which can simultaneously generate both visual representations and semantic representations so that the essential multi-modal information associated with unseen classes can be captured. Specifically, we address the most challenging issue in such a paradigm, i.e., how to handle the domain shift and thus guarantee that the learned representations are modality-invariant. To this end, we propose two strategies: 1) leveraging the mutual information between the latent visual representations and the semantic representations; 2) maximizing the entropy of the joint distribution of the two latent representations. By leveraging the two strategies, we argue that the two modalities can be well aligned. At last, extensive experiments on five widely used datasets verify that the proposed method is able to significantly outperform previous the state-of-the-arts.

Previous Person Re-Identification (Re-ID) models aim to focus on the most discriminative region of an image, while its performance may be compromised when that region is missing caused by camera viewpoint changes or occlusion. To solve this issue, we propose a novel model named Hierarchical Bi-directional Feature Perception Network (HBFP-Net) to correlate multi-level information and reinforce each other. First, the correlation maps of cross-level feature-pairs are modeled via low-rank bilinear pooling. Then, based on the correlation maps, Bi-directional Feature Perception (BFP) module is employed to enrich the attention regions of high-level feature, and to learn abstract and specific information in low-level feature. And then, we propose a novel end-to-end hierarchical network which integrates multi-level augmented features and inputs the augmented low- and middle-level features to following layers to retrain a new powerful network. What's more, we propose a novel trainable generalized pooling, which can dynamically select any value of all locations in feature maps to be activated. Extensive experiments implemented on the mainstream evaluation datasets including Market-1501, CUHK03 and DukeMTMC-ReID show that our method outperforms the recent SOTA Re-ID models.

Fine-grained visual recognition, which aims to identify subcategories of the same base-level category, is a challenging task because of its large intra-class variances and small inter-class variances. Human beings can perform object recognition task based on not only the visual appearance but also the knowledge from texts, as texts can point out the discriminative parts or characteristics which are always the key to distinguishing different subcategories. This is an involuntary transfer from human textual attention to visual attention, suggesting that texts are able to assist fine-grained recognition. In this paper, we propose a Text-Embedded Bilinear (TEB) model which incorporates texts as extra guidance for fine-grained recognition. Specially, we first conduct a text-embedded network to embed text feature into the discriminative image feature learning to get a embedded feature. In addition, since the cross-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other, we also extract a candidate feature from the text encoder and embed it into the inter-layer feature of the image encoder to get an embedded candidate feature. At last we utilize a cross-layer bilinear network to fuse the two embedded features. Comparing with state-of-the-art methods on the widely used CUB-200-2011 dataset and Oxford Flowers-102 dataset for fine-grained image recognition, the experimental results demonstrate our TEB model achieves the best performance.

A large number of recent studies on adversarial attack have verified that a Deep Neural Network (DNN) model designed for non-sequential recognition (NSR) tasks (e.g., classification, detection and segmentation) can be easily fooled by adversarial examples. However, only a few researches pay attention to the adversarial attack on sequential recognition (SR). They either apply the attack methods proposed for NSR to SR by neglecting the sequential dependencies, or focus on attacking specific SR models without considering the generality. In this paper, we study the adversarial attack on the general and popular DNN structure of CNN+RNN, i.e., the combination of convolutional neural network (CNN) and recurrent neural network (RNN), which has been widely used in various SR tasks. We take the scene text recognition (STR) and image captioning (IC) as case study, and derive the objective function for attacking the CNN+RNN based models with targeted and untargeted attack modes, and then developed an optimization-based algorithm to learn adversarial perturbations from the derived gradients of each character (or word) in sequence by incorporating the sequential dependencies. Extensive experiments show that our proposed method can effective fool several state-of-the-arts including four STR models and two IC models with higher successful rate and less time consumption, comparing to three latest attack methods.

Small data challenges have emerged in many learning problems, since the success of deep neural networks often relies on the availability of a huge number of labeled data that is expensive to collect. We explore a highly challenging task, few-sample training, which uses a small number of labeled images of each category and corresponding textual descriptions to train a model for fine-grained visual categorization. In order to tackle overfitting caused by small data, in this paper, we propose two novel feature augmentation approaches, Semantic Gate Feature Augmentation (SGFA) and Semantic Boundary Feature Augmentation (SBFA). Instead of generating a new image instance, we propose to directly synthesize instance features by leveraging semantic information, and its main novelties are: (1) The SGFA method is proposed to reduce the overfitting of small data by adding random noise to different regions of the image's feature maps through a gating mechanism. (2) The SBFA approach is proposed to optimize the decision boundary of the classifier. Technically, the decision boundary of the image feature is estimated through the assistance of semantic information, and then feature augmentation is performed by sampling in this region. Experiments in fine-grained visual categorization benchmark demonstrate that our proposed approach can significantly improve the categorization performance.

Referring expression comprehension is a task of identifying a text-related object or region in a given image by a natural language expression. In this task, it is essential to understand the expression sentence in multi-aspect and adapt it to region representations for generating the discriminative information. Unfortunately, previous approaches usually focus on the important words or phrases in the expression using self-attention mechanisms, which causes that they may fail to distinguish the target region from others, especially the similar regions. To address this problem, we propose a novel model, termed Multi-level Expression Guided Attention network (MEGA-Net). It contains a multi-level visual attention schema guided by the expression representations in different levels, i.e., sentence-level, word-level and phrase-level, which allows generating the discriminative region features and helps to locate the related regions accurately. In addition, to distinguish the similar regions, we design a two-stage structure, where we first select top-K candidate regions according to their matching scores in the first stage, then we apply an object comparison attention mechanism to learn the difference between the candidates for matching the target region. We evaluate the proposed approach on three popular benchmark datasets and the experimental results demonstrate that our model performs against state-of-the-art methods.

Cross-modal retrieval aims at enabling flexible retrieval across different modalities. The core of cross-modal retrieval is to learn projections for different modalities and make instances in the learned common subspace comparable to each other. Self-supervised learning automatically creates a supervision signal by transformation of input data and learns semantic features by training to predict the artificial labels. In this paper, we proposed a novel method named Self-Supervised Adversarial Learning (SSAL) for Cross-Modal Retrieval, which deploys self-supervised learning and adversarial learning to seek an effective common subspace. A feature projector tries to generate modality-invariant representations in the common subspace that can confuse an adversarial discriminator consists of two classifiers. One of the classifiers aims to predict rotation angle from image representations, while the other classifier tries to discriminate between different modalities from the learned embeddings. By confusing the self-supervised adversarial model, feature projector filters out the abundant high-level visual semantics and learns image embeddings that are better aligned with text modality in the common subspace. Through the joint exploitation of the above, an effective common subspace is learned, in which representations of different modlities are aligned better and common information of different modalities is well preserved. Comprehensive experimental results on three widely-used benchmark datasets show that the proposed method is superior in cross-modal retrieval and significantly outperforms the existing cross-modal retrieval methods.

Zero-shot learning has been a highlighted research topic in both vision and language areas. Recently, generative methods have emerged as a new trend of zero-shot learning, which synthesizes unseen categories samples via generative models. However, the lack of fine-grained information in the synthesized samples makes it difficult to improve classification accuracy. It is also time-consuming and inefficient to synthesize samples and using them to train classifiers. To address such issues, we propose a novel Graph-based Variational Auto-Encoder for zero-shot learning. Specifically, we adopt knowledge graph to model the explicit inter-class relationships, and design a full graph convolution auto-encoder framework to generate the classifier from the distribution of the class-level semantic features on individual nodes. The encoder learns the latent representations of individual nodes, and the decoder generates the classifiers from latent representations of individual nodes. In contrast to synthesize samples, our proposed method directly generates classifiers from the distribution of the class-level semantic features for both seen and unseen categories, which is more straightforward, accurate and computationally efficient. We conduct extensive experiments and evaluate our method on the widely used large-scale ImageNet-21K dataset. Experimental results validate the efficacy of the proposed approach.

Scene graph generation intends to build graph-based representation from images, where nodes and edges respectively represent objects and relationships between them. However, scene graph generation today is heavily limited by imbalanced class prediction. Specifically, most of existing work achieves satisfying performance on simple and frequent relation classes (e.g. on), yet leaving poor performance with fine-grained and infrequent ones (e.g. walk on, stand on). To tackle this problem, in this paper, we redesign the framework as two branches, representation learning branch and classifier learning branch, for a more balanced scene graph generator. Furthermore, for representation learning branch, we propose Cross-modal Attention Coordinator (CAC) to gather consistent features from multi-modal using dynamic attention. For classifier learning branch, we first transfer relation classes' knowledge from large scale corpus, then we leverage Multi-Relationship classifier via Graph Attention neTworks (MR-GAT) to bridge the gap between frequent relations and infrequent ones. The comprehensive experimental results on VG200, a challenge dataset, indicate the competitiveness and the significant superiority of our proposed approach.

The human action is an important information source for human social interaction, and it simultaneously plays a crucial role in human-robot interaction (HRI). For a natural and fluent interaction, robots are required to understand human actions and have the capacity to predict action intentions and to imitate human actions for an appropriate response. Currently, existing survey papers for the action recognition mainly summarize algorithms that perform action recognition in experimental scenarios, and survey papers of the HRI mainly introduced various interaction interfaces in the HRI. Different from these surveys, we focus on the human action analysis on robot platforms for the HRI application, including the body motion and gestures. We review the existing HRI related references involving the action recognition, prediction, and the robot imitation of the human action. Moreover, we give a summary of robot platforms and action datasets that are frequently used in the study of HRI. Finally, we give an analysis on the development trend and future research directions of action analysis for the HRI applications.

Recently, learning to hash has been widely studied for image retrieval thanks to the computation and storage efficiency of binary codes. Most existing learning to hash methods have yielded significant performance. However, for most existing learning to hash methods, sufficient training images are required and used to learn precise hashing codes. In some real-world applications, there are not always sufficient training images in the domain of interest. In addition, some existing supervised approaches need a amount of labeled data, which is an expensive process in terms of time, labor and human expertise. To handle such problems, inspired by transfer learning, we propose a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH) where we borrow the images of other different but related domain i.e., source domain to help learn precise hashing codes for the domain of interest i.e., target domain. In GTH, we aim to learn domain-invariant hashing functions. To achieve that, we propose to minimize the error matrix between two hashing projections of target and source domains. We seek for the maximum likelihood estimation (MLE) solution of the error matrix between the two hashing projections due to the domain gap. Furthermore, an alternating optimization method is adopted to obtain the two projections of target and source domains. By doing so, two projections can be progressively aligned. Extensive experiments on various benchmark databases for cross-domain visual recognition verify that our method outperforms many state-of-the-art learning to hash methods. The source code is available at https://github.com/liuji93/GTH.

Unsupervised image hashing has recently gained significant momentum due to the scarcity of reliable supervision knowledge, such as class labels and pairwise relationship. Previous unsupervised methods heavily rely on constructing sufficiently large affinity matrix for exploring the geometric structure of data. Nevertheless, due to lack of adequately preserving the intrinsic information of original visual data, satisfactory performance can hardly be achieved. In this article, we propose a novel approach, called bidirectional discrete matrix factorization hashing (BDMFH), which alternates two mutually promoted processes of 1) learning binary codes from data and 2) recovering data from the binary codes. In particular, we design the inverse factorization model, which enforces the learned binary codes inheriting intrinsic structure from the original visual data. Moreover, we develop an efficient discrete optimization algorithm for the proposed BDMFH. Comprehensive experimental results on three large-scale benchmark datasets show that the proposed BDMFH not only significantly outperforms the state-of-the-arts but also provides the satisfactory computational efficiency.

Given a query instance from one modality (e.g., image), cross-modal retrieval aims to find semantically similar instances from another modality (e.g., text). To perform cross-modal retrieval, existing approaches typically learn a common semantic space from a labeled source set and directly produce common representations in the learned space for the instances in a target set. These methods commonly require that the instances of both two sets share the same classes. Intuitively, they may not generalize well on a more practical scenario of zero-shot cross-modal retrieval, that is, the instances of the target set contain unseen classes that have inconsistent semantics with the seen classes in the source set. Inspired by zero-shot learning, we propose a novel model called ternary adversarial networks with self-supervision (TANSS) in this paper, to overcome the limitation of the existing methods on this challenging task. Our TANSS approach consists of three paralleled subnetworks: 1) two semantic feature learning subnetworks that capture the intrinsic data structures of different modalities and preserve the modality relationships via semantic features in the common semantic space; 2) a self-supervised semantic subnetwork that leverages the word vectors of both seen and unseen labels as guidance to supervise the semantic feature learning and enhances the knowledge transfer to unseen labels; and 3) we also utilize the adversarial learning scheme in our TANSS to maximize the consistency and correlation of the semantic features between different modalities. The three subnetworks are integrated in our TANSS to formulate an end-to-end network architecture which enables efficient iterative parameter optimization. Comprehensive experiments on three cross-modal datasets show the effectiveness of our TANSS approach compared with the state-of-the-art methods for zero-shot cross-modal retrieval.

Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity graph has been extensively studied for large-scale image retrieval. However, most graph-based hashing methods resort to intractable binary quadratic programs, making them unscalable to massive data. In this paper, we propose a novel graph convolutional network-based hashing framework, dubbed GCNH, which directly carries out spectral convolution operations on both an image set and an affinity graph built over the set, naturally yielding similarity-preserving binary embedding. GCNH fundamentally differs from conventional graph hashing methods which adopt an affinity graph as the only learning guidance in an objective function to pursue the binary embedding. As the core ingredient of GCNH, we introduce an intuitive asymmetric graph convolutional (AGC) layer to simultaneously convolve the anchor graph, input data, and convolutional filters. By virtue of the AGC layer, GCNH well addresses the issues of scalability and out-of-sample extension when leveraging affinity graphs for hashing. As a use case of our GCNH, we particularly study the semisupervised hashing scenario in this paper. Comprehensive image retrieval evaluations on the CIFAR-10, NUS-WIDE, and ImageNet datasets demonstrate the consistent advantages of GCNH over the state-of-the-art methods given limited labeled data.

Recent years have witnessed the wide application of hashing for large-scale image retrieval, because of its high computation efficiency and low storage cost. Particularly, benefiting from current advances in deep learning, supervised deep hashing methods have greatly boosted the retrieval performance, under the strong supervision of large amounts of manually annotated semantic labels. However, their performance is highly dependent upon the supervised labels, which significantly limits the scalability. In contrast, unsupervised deep hashing without label dependence enjoys the advantages of well scalability. Nevertheless, due to the relaxed hash optimization, and more importantly, the lack of semantic guidance, existing methods suffer from limited retrieval performance. In this paper, we propose a SCAlable Deep Hashing (SCADH) to learn enhanced hash codes for social image retrieval. We formulate a unified scalable deep hash learning framework which explores the weak but free supervision of discriminative user tags that are commonly accompanied with social images. It jointly learns image representations and hash functions with deep neural networks, and simultaneously enhances the discriminative capability of image hash codes with the refined semantics from the accompanied social tags. Further, instead of simple relaxed hash optimization, we propose a discrete hash optimization method based on Augmented Lagrangian Multiplier to directly solve the hash codes and avoid the binary quantization information loss. Experiments on two standard social image datasets demonstrate the superiority of the proposed approach compared with stateof- the-art shallow and deep hashing techniques.

Human actions involve a wide variety and a large number of categories, which leads to a big challenge in action recognition. However, according to similarities on human body poses, scenes, interactive objects, human actions can be grouped into some semantic groups, i.e sports, cooking, etc. Therefore, in this paper, we propose a novel approach which recognizes human actions from coarse to fine. Taking full advantage of contributions from high-level semantic contexts, a context knowledge map guided recognition method is designed to realize the coarse-to-fine procedure. In the approach, we define semantic contexts with interactive objects, scenes and body motions in action videos, and build a context knowledge map to automatically define coarse-grained groups. Then fine-grained classifiers are proposed to realize accurate action recognition. The coarse-to-fine procedure narrows action categories in target classifiers, so it is beneficial to improving recognition performance. We evaluate the proposed approach on the CCV, the HMDB-51, and the UCF101 database. Experiments verify its significant effectiveness, on average, improving more than 5% of recognition precisions than current approaches. Compared with the state-of-the-art, it also obtains outstanding performance. The proposed approach achieves higher accuracies of 93.1%, 95.4% and 74.5% in the CCV, the UCF-101 and the HMDB51 database, respectively.

Existing hashing methods have yielded significant performance in image and multimedia retrieval, which can be categorized into two groups: shallow hashing and deep hashing. However, there still exist some intrinsic limitations among them. The former generally adopts a one-step strategy to learn the hashing codes for discovering the discriminative binary feature, but the latent discriminative information in the learned hashing codes is not well exploited. The latter, as deep neural network based hashing models, can learn highly discriminative and compact features, but relies on large-scale data and computation resources for numerous network parameters tuning with back-propagation optimization. Straightforward training of deep hashing models from scratch on small-scale data is almost impossible. Therefore, in order to develop efficient but effective learning to hash algorithm that depends only on small-scale data, we propose a novel non-neural network based deep-like learning framework, i.e. multi-level cascaded hashing (MCH) approach with hierarchical learning strategy, for image retrieval. The contributions are threefold. First, a hashing-in-hash architecture is designed in MCH, which inherits the excellent traits of traditional neural networks based deep learning, such that discriminative binary features that are beneficial to image retrieval can be effectively captured. Second, in each level the binary features of all preceding levels and the visual appearance feature are simultaneously cascaded as inputs of all subsequent levels to retrain, which fully exploits the implicated discriminative information. Third, a basic learning to hash (BLH) model with label constraint is proposed for hierarchical learning. Without loss of generality, the existing hashing models can be easily integrated into our MCH framework. We show experimentally on small- and large-scale visual retrieval tasks that our method outperforms several state-of-the-arts.

This paper introduces a new model for Weakly Supervised Object Localization (WSOL) problems where only image-level supervision is provided. The key to solve such problems is to infer the object locations accurately. Previous methods usually model the missing object locations as latent variables, and alternate between updating their estimates and learning a detector accordingly. However, the performance of such alternative optimization is sensitive to the quality of the initial latent variables and the resulted localization model is prone to overfitting to improper localizations. To address these issues, we develop a Prior-induced Multi-view Learning Localization Network (PML-LocNet) which exploits both view diversity and sample diversity to improve object localization. In particular, the view diversity is imposed by a two-phase multi-view learning strategy, with which the complementarity among learned features from different views and the consensus among localized instances from each view are leveraged to benefit localization. The sample diversity is pursued by harnessing coarse-to-fine priors at both image and instance levels. With these priors, more emphasis would go to the reliable samples and the contributions of the unreliable ones would be decreased, such that the intrinsic characteristics of each sample can be exploited to make the model more robust during network learning. PML-LocNet can be easily combined with existing WSOL models to further improve the localization accuracy. Its effectiveness has been proved experimentally. Notably, it achieves 69.3% CorLoc and 50.4% mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.

It has been demonstrated that the segmentation performance is highly dependent on both subspace preservation and graph connectivity. In the literature, the full connectivity method linearly represents each data point ( e.g., a pixel in one image) by all data points for achieving subspace preservation, while the sparse connectivity method was designed to linearly represent each data point by a set of data points for achieving graph connectivity. However, previous methods only focused on either subspace preservation or graph connectivity. In this article, we propose a Sparse Graph Connectivity (SGC) method for image segmentation to automatically learn the affinity matrix from the low-dimensional space of original data, which aims at simultaneously achieving subspace preservation and graph connectivity. To do this, the proposed SGC simultaneously learns a self-representation affinity matrix for subspace preservation and a sparse affinity matrix for graph connectivity, from the intrinsic low-dimensional feature space of high-dimensional original data. Meanwhile, the self-representation affinity matrix is pushed to be similar to the sparse affinity as well as be the final segmentation results. Experimental result on synthetic and real-image datasets showed that our SGC method achieved the best segmentation performance, compared to state-of-the-art segmentation methods.

Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering methods cannot effectively detect these information so that difficultly outputting reliable clustering models. In this article, we propose a fuzzy, sparse, and robust multi-view clustering method to consider all kinds of relations among the data (such as view importance, view stability, and view diversity), which can effectively extract both distinct information and common information as well as balance these two kinds of information. Moreover, we devise an alternating optimization algorithm to solve the resulting objective function as well as prove that our proposed algorithm achieves fast convergence. It is noteworthy that existing multi-view clustering methods only consider a part of the relations, and thus are a special case of our proposed framework. Experimental results on synthetic datasets and real datasets show that our proposed method outperforms the state-of-the-art clustering methods in terms of evaluation metrics of clustering such as clustering accuracy, normalized mutual information, purity, and adjusted rand index.

With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management. In this paper, we analyze the impact of 5G on both traditional and emerging technologies and project our view on future research challenges and opportunities. With a predicted increase of 10-100× in bandwidth and 5-10x decrease in latency, 5G is expected to be the main enabler for smart cities, smart IoT and efficient healthcare, where machine learning is conducted at the edge. In this context, we investigate how 5G can help the development of federated learning. Network slicing, another key feature of 5G, allows running multiple isolated networks on the same physical infrastructure. However, security remains the main concern in the context of virtualization, multi-tenancy and high device density. Formal verification of 5G networks can be applied to detect security issues in massive virtualized environments. In summary, 5G will make the world even more densely and closely connected. What we have experienced in 4G connectivity will pale in comparison to the vast amounts of possibilities engendered by 5G.

The task of image-text matching refers to measuring the visual-semantic similarity between an image and a sentence. Recently, the fine-grained matching methods that explore the local alignment between the image regions and the sentence words have shown advance in inferring the image-text correspondence by aggregating pairwise region-word similarity. However, the local alignment is hard to achieve as some important image regions may be inaccurately detected or even missing. Meanwhile, some words with high-level semantics cannot be strictly corresponding to a single-image region. To tackle these problems, we address the importance of exploiting the global semantic consistence between image regions and sentence words as complementary for the local alignment. In this article, we propose a novel hybrid matching approach named Cross-modal Attention with Semantic Consistency (CASC) for image-text matching. The proposed CASC is a joint framework that performs cross-modal attention for local alignment and multilabel prediction for global semantic consistence. It directly extracts semantic labels from available sentence corpus without additional labor cost, which further provides a global similarity constraint for the aggregated region-word similarity obtained by the local alignment. Extensive experiments on Flickr30k and Microsoft COCO (MSCOCO) data sets demonstrate the effectiveness of the proposed CASC on preserving global semantic consistence along with the local alignment and further show its superior image-text matching performance compared with more than 15 state-of-the-art methods.

This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision. We model the missing object locations as latent variables, and contribute a novel self-directed optimization strategy to infer them. With the strategy, our developed Self-Directed Localization Network (SD-LocNet) is able to localize object instance whose initial location is noisy. The self-directed inference hinges on an adaptive sampling method to identify reliable object instance via measuring its localization stability score. In this way, the resulted model is robust to noisy initialized object locations which we find is important in WSOL. Furthermore, we introduce a reliability induced prior propagation strategy to transfer object priors of the reliable instances to those unreliable ones by promoting their feature similarity, which effectively refines the unreliable object instances for better localization. The proposed SD-LocNet achieves 70.9% Cor-Loc and 51.3% mAP on PASCAL VOC 2007, surpassing the state-of-the-arts by a large margin.

Inferring the interactions between objects, a.k.a visual relationship detection, is a crucial point for vision understanding, which captures more definite concepts than object detection. Most previous work that treats the interaction between a pair of objects as a one way fail to exploit the mutual relation between objects, which is essential to modern visual application. In this work, we propose a mutual relation net, dubbed MR-Net, to explore the mutual relation between paired objects for visual relationship detection. Specifically, we construct a mutual relation space to model the mutual interaction of paired objects, and employ linear constraint to optimize the mutual interaction, which is called mutual relation learning. Our mutual relation learning does not introduce any parameters, and can adapt to improve the performance of other methods. In addition, we devise a semantic ranking loss to discriminatively penalize predicates with semantic similarity, which is ignored by traditional loss function (e.g., cross entropy with softmax). Then, our MR-Net optimizes the mutual relation learning together with semantic ranking loss with a siamese network. The experimental results on two commonly used datasets (VG and VRD) demonstrate the superior performance of the proposed approach.

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.

Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face changes substantially over time, resulting in significant intraclass variations. As opposed to current techniques for ageinvariant face recognition, which either directly extract ageinvariant features for recognition, or first synthesize a face that matches target age before feature extraction, we argue that it is more desirable to perform both tasks jointly so that they can leverage each other. To this end, we propose a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties. First, AIM presents a novel unified deep architecture jointly performing cross-age face synthesis and recognition in a mutual boosting way. Second, AIM achieves continuous face rejuvenation/aging with remarkable photorealistic and identity-preserving properties, avoiding the requirement of paired data and the true age of testing samples. Third, we develop effective and novel training strategies for end-to-end learning the whole deep architecture, which generates powerful age-invariant face representations explicitly disentangled from the age variation. Extensive experiments on several cross-age datasets (MORPH, CACD and FG-NET) demonstrate the superiority of the proposed AIM model over the state-of-the-arts. Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.

Siamese networks recently have caused great attention in video tracking community. It successfully achieves not only real-time tracking, but also high precision. However, the typical Siamese networks are relatively mean performance to the target of scale variation, rapid deformation and etc due to the operation on the central point of the object only, rather than the entire spatial information. In this paper, we propose a spatial-aware video tracking method called SiamRFC to predict the central point of the tracked object and estimate its spatial configuration simultaneously. Specifically, our SiamRFC firstly predicts the central point of tracked object via Siamese network by judging the similarity. According to this central, we further estimate its optimal anchor configuration of the tracked object among multiple candidates, and then based on the optimal anchor to regress target position. Extensive evaluations on VOT2015, VOT2016 and VOT2017 benchmarks demonstrate that the proposed SiamRFC tracker is competitive with state-ofthe-art trackers.

With wide applications in surveillance and human-robot interaction, view-invariant human action recognition is critical, however, challenging, due to the action occlusion and information loss caused by view change. Current methods mainly seek for a common feature space for different views. However, such solutions become invalid when there exist few common features, e.g. large view change. To tackle the problem, we propose an AttentioN Transfer (ANT) Network for view-invariant action recognition. Other than transferring features, ANT transfers attention from the reference view to arbitrary views, which correctly emphasize crucial body joints and their relations for view-invariant representation. In addition, the attention calculation method taking into account both recognition contribution and reliability of skeleton joints generates effective attention. Experiments showed its effectiveness for correctly locating crucial body joints in action sequences. We exhaustively evaluate our approach on the UESTC and the NTU dataset with three types of view-invariant evaluations, i.e. X-view, X-sub, and Arbitrary-view evaluation. Experiment results demonstrate its superiority in view-invariant representation and recognition.

Lately, generative adversarial networks (GANs) have been successfully applied to zero-shot learning (ZSL) and achieved state-of-the-art performance. By synthesizing virtual unseen visual features, GAN-based methods convert the challenging ZSL task into a supervised learning problem. However, since real unseen visual features are not available at the training stage, GAN-based ZSL methods have to train the GAN generator on the seen categories and further apply it to unseen instances. An inevitable issue of such a paradigm is that the synthesized unseen features are prone to seen references and incapable to reflect the novelty and diversity of real unseen instances. In a nutshell, the synthesized features are confusing. One cannot tell unseen categories from seen ones using the synthesized features. As a result, the synthesized features are too subtle to be classified in generalized zero-shot learning (GZSL) which involves both seen and unseen categories at the test stage. In this paper, we first introduce the feature confusion issue. Then, we propose a new feature generating network, named alleviating feature confusion GAN (AFC-GAN), to challenge the issue. Specifically, we present a boundary loss which maximizes the decision boundary of seen categories and unseen ones. Furthermore, a novel metric named feature confusion score (FCS) is proposed to quantify the feature confusion. Extensive experiments on five widely used datasets verify that our method is able to outperform previous state-of-the-arts under both ZSL and GZSL protocols.

Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way. In this paper, we address three critical challenges for this task in a reinforcement learning setting: the mode collapse, the delayed feedback, and the time-consuming warm-up for policy networks. Generally, we propose a novel Curiosity-driven Reinforcement Learning (CRL) framework to jointly enhance the diversity and accuracy of the generated paragraphs. First, by modeling the paragraph captioning as a long-term decision-making process and measuring the prediction uncertainty of state transitions as intrinsic rewards, the model is incentivized to memorize precise but rarely spotted descriptions to context, rather than being biased towards frequent fragments and generic patterns. Second, since the extrinsic reward from evaluation is only available until the complete paragraph is generated, we estimate its expected value at each time step with temporal-difference learning, by considering the correlations between successive actions. Then the estimated extrinsic rewards are complemented by dense intrinsic rewards produced from the derived curiosity module, in order to encourage the policy to fully explore action space and find a global optimum. Third, discounted imitation learning is integrated for learning from human demonstrations, without separately performing the time-consuming warm-up in advance. Extensive experiments conducted on the Standford image-paragraph dataset demonstrate the effectiveness and efficiency of the proposed method, improving the performance by 38.4% compared with state-of-the-art.

The task of Visual Question Answering (VQA) is to answer a natural language question tied to the content of a visual image. Most existing VQA models either apply attention mechanism to locate the relevant object regions and/or utilize the off-the-shelf methods of the relation reasoning to detect object relations. However, they 1) mostly encode the simple relations which cannot sufficiently provide sophisticated knowledge for answering complicated visual questions; 2) seldom leverage the harmony cooperation of the object appearance feature and relation feature. To address these problems, we propose a novel end-to-end VQA model, termed Composed Relation Attention Network (CRA-Net ). In specific, we devise two question-adaptive relation attention modules that can extract not only the fine-grained and precise binary relations but also the more sophisticated trinary relations. Both kinds of question-related relations can reveal deeper semantics, thereby enhancing the reasoning ability in question answering. Furthermore, our CRA-Net also combines the object appearance feature with the relation feature under the guidance of the corresponding question, which can reconcile the two types of features effectively. Extensive experiments on two large benchmark datasets, VQA-1.0 and VQA-2.0, demonstrate that our proposed model outperforms state-of-the-art approaches.

A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on embedding or classification, the first one mapping image and text instances into a common embedding space for distance measuring, and the second one regarding image-text matching as a binary classification problem. Neither of these approaches can, however, balance the matching accuracy and model complexity well. We propose a novel framework that achieves remarkable matching performance with acceptable model complexity. Specifically, in the training stage, we propose a novel Multi-modal Tensor Fusion Network (MTFN) to explicitly learn an accurate image-text similarity function with rank-based tensor fusion rather than seeking a common embedding space for each image-text instance. Then, during testing, we deploy a generic Cross-modal Re-ranking (RR) scheme for refinement without requiring additional training procedure. Extensive experiments on two datasets demonstrate that our MTFN-RR consistently achieves the state-of-the-art matching performance with much less time complexity.

Most existing Zero-Shot Learning (ZSL) approaches adopt the semantic space as a bridge to classify unseen categories. However, it is difficult to transfer knowledge from seen categories to unseen categories through semantic space, since the correlations among categories are uncertain and ambiguous in the semantic space. In this paper, we formulated zero-shot learning as a classifier weight regression problem. Specifically, we propose a novel Residual Graph Convolution Network (ResGCN) which takes word embeddings and knowledge graph as inputs and outputs a visual classifier for each category. ResGCN can effectively alleviate the problem of over-smoothing and over-fitting. During the test, an unseen image can be classified by ranking the inner product of its visual feature and predictive visual classifiers. Moreover, we provide a new method to build a better knowledge graph. Our approach not only further enhances the correlations among categories, but also makes it easy to add new categories to the knowledge graph. Experiments conducted on the large-scale ImageNet 2011 21K dataset demonstrate that our method significantly outperforms existing state-of-the-art approaches.

Recently, a new form of telexistence is achieved by recording images with cameras on an unmanned aerial vehicle (UAV) and displaying them to the user via a head mounted display (HMD). A key problem here is how to provide a free and natural mechanism for the user to control the viewpoint and watch a scene. To this end, we propose an improved rate-control method with an adaptive origin update (AOU) scheme. Without the aid of any auxiliary equipment, our scheme handles the self-centering problem. In addition, we present a full 6-DOF viewpoint control method to manipulate the motion of a stereo camera, and we build a real prototype to realize this by utilizing a pan-tilt-zoom (PTZ) which not only provides 2-DOF to the camera but also compensates the jittering motion of the UAV to record more stable image streams.

Video captioning has been attracting broad research attention in the multimedia community. However, most existing approaches heavily rely on static visual information or partially capture the local temporal knowledge (e.g., within 16 frames), thus hardly describing motions accurately from a global view. In this paper, we propose a novel video captioning framework, which integrates bidirectional long-short term memory (BiLSTM) and a soft attention mechanism to generate better global representations for videos as well as enhance the recognition of lasting motions in videos. To generate video captions, we exploit another long-short term memory as a decoder to fully explore global contextual information. The benefits of our proposed method are two fold: 1) the BiLSTM structure comprehensively preserves global temporal and visual information and 2) the soft attention mechanism enables a language decoder to recognize and focus on principle targets from the complex content. We verify the effectiveness of our proposed video captioning framework on two widely used benchmarks, that is, microsoft video description corpus and MSR-video to text, and the experimental results demonstrate the superiority of the proposed approach compared to several state-of-the-art methods.

In this paper, we study the problem of cross-modal retrieval by hashing-based approximate nearest neighbor (ANN) search techniques. Most existing cross-modal hashing work mainly addresses the issue of multi-modal integration complexity using the same mapping and similarity calculation for data from different media types. Nonetheless, this may cause information loss during the mapping process due to overlooking the specifics of each individual modality. In this work, we propose a simple yet effective cross-modal hashing approach, termed Collective Reconstructive Embeddings (CRE), which can simultaneously solve the heterogeneity and integration complexity of multi-modal data. To address the heterogeneity challenge, we propose to process heterogeneous types of data using different modalityspecific models. Specifically, we model textual data with cosine similarity based reconstructive embedding to alleviate the data sparsity to the greatest extent, while for image data we utilize the Euclidean distance to characterize the relationships of the projected hash codes. Meanwhile, we unify the projections of text and image to the Hamming space into a common reconstructive embedding through rigid mathematical reformulation, which not only reduces the optimization complexity significantly but also facilitates the inter-modal similarity preservation among different modalities. We further incorporate the code balance and uncorrelation criteria into the problem, and devise an efficient iterative algorithm for optimization. Comprehensive experiments on four widely-used multimodal benchmarks show that the proposed CRE can achieve superior performance compared to the state-of-the-arts on several challenging cross-modal tasks.

Zero-shot learning aims to classify visual instances from unseen classes in the absence of training examples. This is typically achieved by directly mapping visual features to a semantic embedding space of classes (e.g., attributes or word vectors), where the similarity between the two modalities can be readily measured. However, the semantic space may not be reliable for recognition due to the noisy class embeddings or visual bias problem. In this work, we propose a novel Binary embedding based Zero-Shot Learning (BZSL) method, which recognizes visual instances from unseen classes through an intermediate discriminative Hamming space. Specifically, BZSL jointly learns two binary coding functions to encode both visual instances and class embeddings into the Hamming space, which well alleviates the visual-semantic bias problem. As a desiring property, classifying an unseen instance thereby can be efficiently done by retrieving its nearest-class codes with minimal Hamming distance. During training, by introducing two auxiliary variables for the coding functions, we formulate an equivalent correlation maximization problem, which admits an analytical solution. The resulting algorithm thus enjoys both highly efficient training and scalable novel class inferring. Extensive experiments on four benchmark datasets, including the full ImageNet Fall 2011 dataset with over 20K unseen classes, demonstrate the superiority of our method on the zero-shot learning task. Particularly, we show that increasing the binary embedding dimension can inevitably improve the recognition accuracy.

Recently, a great progress in automatic image captioning has been achieved by using semantic concepts detected from the image. However, we argue that existing concepts-to-caption framework, in which the concept detector is trained using the image-caption pairs to minimize the vocabulary discrepancy, suffers from the deficiency of insufficient concepts. The reasons are two-fold: 1) the extreme imbalance between the number of occurrence positive and negative samples of the concept and 2) the incomplete labeling in training captions caused by the biased annotation and usage of synonyms. In this paper, we propose a method, termed online positive recall and missing concepts mining, to overcome those problems. Our method adaptively re-weights the loss of different samples according to their predictions for online positive recall and uses a two-stage optimization strategy for missing concepts mining. In this way, more semantic concepts can be detected and a high accuracy will be expected. On the caption generation stage, we explore an element-wise selection process to automatically choose the most suitable concepts at each time step. Thus, our method can generate more precise and detailed caption to describe the image. We conduct extensive experiments on the MSCOCO image captioning data set and the MSCOCO online test server, which shows that our method achieves superior image captioning performance compared with other competitive methods.

Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scene spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, in order to cope with the problem of data sparsity, we propose to comprehensively describe POI using various types of user generated content (UGC) (e.g., text and image) from multiple sources. Then, we devise an effective POI modelling method in a hierarchical manner, which simultaneously injects semantic knowledge as well as multi-clue representative power into POIs. For evaluation, we construct a multi-source POI dataset by collecting all the textual and visual content of several specific provinces in China from four main-stream tourism platforms during 2006 to 2017. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions' popularity as compared to several baseline methods.

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.

Recently, automatic answering of visually related questions (VQA) has gained a lot of attention in computer vision community. However, there is little work on automatically generating questions for images (VQG). Actually, VQG itself closes the loop to question-answering and diverse questions, which is useful to the research on VQA. Motivated by the assumption that learning to answer questions may boost the question generation, in this paper, we introduce the VQA task as the complementary of our primary VQG task, and propose a novel model that uses dual learning framework to jointly learn the dual tasks. In the framework, we devise an agent for VQG and VQA with pre-trained models respectively, and the learning tasks of the two agents form a closed loop, whose objectives are optimized together to guide each other via a reinforcement learning process. Specific rewards for each task are designed to update the models of the agents with policy gradient method. The relation of these two tasks can be exploited to further improve the performance of the primary VQG task. Extensive experiments conducted on two large-scale datasets show that the proposed method is capable to generate grounded visual questions of sufficient coverage and outperforms previous VQG methods on standard measures.

Cross-modal retrieval, e.g., using an image query to search related text and vice-versa, has become a highlighted research topic, to provide flexible retrieval experience across multi-modal data. Existing approaches usually consider the so-called non-extendable cross-modal retrieval task. In this task, they learn a common latent subspace from a source set containing labeled instances of image-text pairs and then generate common representation for the instances in a target set to perform cross-modal matching. However, these method may not generalize well when the instances of the target set contains unseen classes since the instances of both the source and target set are assumed to share the same range of classes in the non-extensive cross-modal retrieval task. In this paper, we consider a more practical issue of extendable cross-modal retrieval task where instances in source and target set have disjoint classes. We propose a novel framework, termed Modal-adversarial Semantic Learning Network (MASLN), to tackle the limitation of existing methods on this practical task. Specifically, the proposed MASLN consists two subnetworks of cross-modal reconstruction and modal-adversarial semantic learning. The former minimizes the cross-modal distribution discrepancy by reconstructing each modality data mutually, with the guidelines of class embeddings as side information in the reconstruction procedure. The latter generates semantic representation to be indiscriminative for modalities, while to distinguish the modalities from the common representation via an adversarial learning mechanism. The two subnetworks are jointly trained to enhance the cross-modal semantic consistency in the learned common subspace and the knowledge transfer to instances in the target set. Comprehensive experiment on three widely-used multi-modal datasets show its effectiveness and robustness on both non-extendable and extendable cross-modal retrieval task.

Visual relationship detection, which aims to predict a triplet with the detected objects, has attracted increasing attention in the scene understanding study. During tackling this problem, dealing with varying scales of the subjects and objects is of great importance, which has been less studied. To overcome this challenge, we propose a novel Vision Spatial Attention Network (VSA-Net), which employs a two-dimensional normal distribution attention scheme to effectively model small objects. In addition, we design a Subject-Object-layer (SO-layer) to distinguish between the subject and object to attain more precise results. To the best of our knowledge, VSA-Net is the first end-to-end attention mechanism based visual relationship detection model. Extensive experiments on the benchmark datasets (VRD and VG) show that, by using pure vision information, our VSA-Net achieves state-of-the-art performance for predicate detection, phrase detection, and relationship detection.

Current researches mainly focus on single-view and multiview human action recognition, which can hardly satisfy the requirements of human-robot interaction (HRI) applications to recognize actions from arbitrary views. The lack of databases also sets up barriers. In this paper, we newly collect a large-scale RGB-D action database for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The database includes action samples captured in 8 fixed viewpoints and varying-view sequences which covers the entire 360 view angles. In total, 118 persons are invited to act 40 action categories, and 25,600 video samples are collected. Our database involves more articipants, more viewpoints and a large number of samples. More importantly, it is the first database containing the entire 360? varying-view sequences. The database provides sufficient data for cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance.

Person re-identification is an important task in video surveillance, focusing on finding the same person across different cameras. However, most existing methods of video-based person re-identification still have some limitations (e.g., the lack of effective deep learning framework, the robustness of the model, and the same treatment for all video frames) which make them unable to achieve better recognition performance. In this paper, we propose a novel self-paced learning algorithm for video-based person re-identification, which could gradually learn from simple to complex samples for a mature and stable model. Self-paced learning is employed to enhance video-based person re-identification based on deep neural network, so that deep neural network and self-paced learning are unified into one frame. Then, based on the trained self-paced learning, we propose to employ deep reinforcement learning to discard misleading and confounding frames and find the most representative frames from video pairs. With the advantage of deep reinforcement learning, our method can learn strategies to select the optimal frame groups. Experiments show that the proposed framework outperforms the existing methods on the iLIDS-VID, PRID-2011 and MARS datasets.

Recent vision and learning studies show that learning compact hash codes can facilitate massive data processing with significantly reduced storage and computation. Particularly, learning deep hash functions has greatly improved the retrieval performance, typically under the semantic supervision. In contrast, current unsupervised deep hashing algorithms can hardly achieve satisfactory performance due to either the relaxed optimization or absence of similarity-sensitive objective. In this work, we propose a simple yet effective unsupervised hashing framework, named Similarity-Adaptive Deep Hashing (SADH), which alternatingly proceeds over three training modules: deep hash model training, similarity graph updating and binary code optimization. The key difference from the widely-used two-step hashing method is that the output representations of the learned deep model help update the similarity graph matrix, which is then used to improve the subsequent code optimization. In addition, for producing high-quality binary codes, we devise an effective discrete optimization algorithm which can directly handle the binary constraints with a general hashing loss. Extensive experiments validate the efficacy of SADH, which consistently outperforms the state-of-the-arts by large gaps.

Large-scale search methods are increasingly critical for many content-based visual analysis applications, among which hashing-based approximate nearest neighbor search techniques have attracted broad interests due to their high efficiency in storage and retrieval. However, existing hashing works are commonly designed for measuring data similarity by the Euclidean distances. In this paper, we focus on the problem of learning compact binary codes using the cosine similarity. Specifically, we proposed novel angular reconstructive embeddings (ARE) method, which aims at learning binary codes by minimizing the reconstruction error between the cosine similarities computed by original features and the resulting binary embeddings. Furthermore, we devise two efficient algorithms for optimizing our ARE in continuous and discrete manners, respectively. We extensively evaluate the proposed ARE on several large-scale image benchmarks. The results demonstrate that ARE outperforms several state-of-the-art methods.

In this paper, we propose a novel approach to video captioning based on adversarial learning and Long-Short Term Memory (LSTM). With this solution concept we aim at compensating for the deficiencies of LSTM-based video captioning methods that generally show potential to effectively handle temporal nature of video data when generating captions, but that also typically suffer from exponential error accumulation. Specifically, we adopt a standard Generative Adversarial Network (GAN) architecture, characterized by an interplay of two competing processes: a "generator", which generates textual sentences given the visual content of a video, and a "discriminator" which controls the accuracy of the generated sentences. The discriminator acts as an "adversary" towards the generator and with its controlling mechanism helps the generator to become more accurate. For the generator module, we take an existing video captioning concept using LSTM network. For the discriminator, we propose a novel realization specifically tuned for the video captioning problem and taking both the sentences and video features as input. This leads to our proposed LSTM-GAN system architecture, for which we show experimentally to significantly outperform the existing methods on standard public datasets.

In a document, the topic distribution of a sentence depends on both the topics of preceding sentences and its own content, and it is usually affected by the topics of the preceding sentences with different weights. It is natural that a document can be treated as a sequence of sentences. Most existing works for Bayesian document modeling do not take these points into consideration. To fill this gap, we propose a Recurrent Attentional Topic Model (RATM) for document embedding. The RATM not only takes advantage of the sequential orders among sentence but also use the attention mechanism to model the relations among successive sentences. In RATM, we propose a Recurrent Attentional Bayesian Process (RABP) to handle the sequences. Based on the RABP, RATM fully utilizes the sequential information of the sentences in a document. Experiments on two copora show that our model outperforms state-of-the-art methods on document modeling and classification.

This paper proposes a one-step spectral clustering method by learning an intrinsic affinity matrix (i.e., the clustering result) from the low-dimensional space (i.e., intrinsic subspace) of original data. Specifically, the intrinsic affinitymatrix is learnt by: 1) the alignment of the initial affinity matrix learnt from original data; 2) the adjustment of the transformation matrix, which transfers the original feature space into its intrinsic subspace by simultaneously conducting feature selection and subspace learning; and 3) the clustering result constraint, i.e., the graph constructed by the intrinsic affinity matrix has exact c connected components where c is the number of clusters. In this way, two affinity matrices and a transformation matrix are iteratively updated until achieving their individual optimum, so that these two affinity matrices are consistent and the intrinsic subspace is learnt via the transformation matrix. Experimental results on both synthetic and benchmark datasets verified that our proposed method outputted more effective clustering result than the previous clustering methods.

Recently, hashing has been evidenced as an efficient and effective method to facilitate large-scale video retrieval. Most of existing hashing methods are based on visual features, which are expected to capture the appearance of videos. The intrinsic temporal pattern embedded in videos has also shown its discriminative power for similarity search, and is explored and utilised in some recent studies. However, how to leverage the strengths in both aspects remains unknown.

Zero-shot learning (ZSL) aims to recognize objects of unseen classes with available training data from another set of seen classes. Existing solutions are focused on exploring knowledge transfer via an intermediate semantic embedding (e.g., attributes) shared between seen and unseen classes. In this paper, we propose a novel projection framework based on matrix tri-factorization with manifold regularizations. Specifically, we learn the semantic embedding projection by decomposing the visual feature matrix under the guidance of semantic embedding and class label matrices. By additionally introducing manifold regularizations on visual data and semantic embeddings, the learned projection can effectively captures the geometrical manifold structure residing in both visual and semantic spaces. To avoid the projection domain shift problem, we devise an effective prediction scheme by exploiting the test-time manifold structure. Extensive experiments on four benchmark datasets show that our approach significantly outperforms the state-of-the-arts, yielding an average improvement ratio by 7.4% and 31.9% for the recognition and retrieval task, respectively.

Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction. In this work, we present a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of the current input video sequences, in a way that interdependency from the matching items can directly influence the computation of each other's representation. Specifically, the spatial pooling layer is able to select regions from each frame, while the attention temporal pooling performed can select informative frames over the sequence, both pooling guided by the information from distance matching. Experiments are conduced on the iLIDS-VID, PRID-2011 and MARS datasets and the results demonstrate that this approach outperforms existing state-of-art methods. We also analyze how the joint pooling in both dimensions can boost the person re-id performance more effectively than using either of them separately 1.

Locality sensitive hashing (LSH) and its variants are widely used for approximate kNN (k nearest neighbor) search in high-dimensional space. The success of these techniques largely depends on the ability of preserving kNN information. Unfortunately, LSH only provides a high probability that nearby points in the original space are projected into nearby region in a new space. This potentially makes many false positives and false negatives resulting from unrelated points. Many extensions of LSH aim to alleviate the above issue by improving the distance preserving ability. In this paper, we abound improving LSH function but propose a novel idea to enhance the performance by transforming the original data to a new space before applying LSH. A preserving-ignoring transformation (PIT) function satisfying some rigorous conditions can be used to convert original points to an interim space with strict distance preserving-ignoring capacity. Based on this property, a linear order is utilized to build an efficient index structure in the interim space. Finally, LSH can be applied to candidate set searched by our index structure for final results. Experiments are conducted and the proposed approach performs better than state-of-the-art methods SK-LSH, DSH and NSH in terms of both accuracy and efficiency.

The core of existing cross-modal retrieval approaches is to close the gap between different modalities either by finding a maximally correlated subspace or by jointly learning a set of dictionaries. However, the statistical characteristics of the transformed features were never considered. Inspired by recent advances in adversarial learning and domain adaptation, we propose a novel Unsupervised Cross-modal retrieval method based on Adversarial Learning, namely UCAL. In addition to maximizing the correlations between modalities, we add an additional regularization by introducing adversarial learning. In particular, we introduce a modality classifier to predict the modality of a transformed feature. This can be viewed as a regularization on the statistical aspect of the feature transforms, which ensures that the transformed features are also statistically indistinguishable. Experiments on popular multimodal datasets show that UCAL achieves competitive performance compared to state of the art supervised cross-modal retrieval methods.

Learning based hashing has become increasingly popular because of its high efficiency in handling the large scale image retrieval. Preserving the pairwise similarities of data points in the Hamming space is critical in state-of-the-art hashing techniques. However, most previous methods ignore to capture the local geometric structure residing on original data, which is essential for similarity search. In this paper, we propose a novel hashing framework, which simultaneously optimizes similarity preserving hash codes and reconstructs the locally linear structures of data in the Hamming space. In specific, we learn two hash functions such that the resulting two sets of binary codes can well preserve the pairwise similarity and sparse neighborhood in the original feature space. By taking advantage of the flexibility of asymmetric hash functions, we devise an efficient alternating algorithm to optimize the hash coding function and high-quality binary codes jointly. We evaluate the proposed method on several large-scale image datasets, and the results demonstrate it significantly outperforms recent state-of-the-art hashing methods on large-scale image retrieval problems.

Hashing has been recognized as one of the most promising ways in indexing and retrieving high-dimensional data due to the excellent merits in efficiency and effectiveness. Nevertheless, most existing approaches inevitably suffer from the problem of “semantic gap”, especially when facing the rapid evolution of newly-emerging “unseen” categories on the Web. In this work, we propose an innovative approach, termed Attribute Hashing (AH), to facilitate zero-shot image retrieval (i.e., query by “unseen” images). In particular, we propose a multi-layer hierarchy for hashing, which fully exploits attributes to model the relationships among visual features, binary codes and labels. Besides, we deliberately preserve the nature of hash codes (i.e., discreteness and local structure) to the greatest extent. We conduct extensive experiments on several real-world image datasets to show the superiority of our proposed AH approach as compared to the state-of-the-arts.

Zero-shot learning (ZSL) aims to bridge the knowledge transfer via available semantic representations (e.g., attributes) between labeled source instances of seen classes and unlabelled target instances of unseen classes. Most existing ZSL approaches achieve this by learning a projection from the visual feature space to the semantic representation space based on the source instances, and directly applying it to the target instances. However, the intrinsic manifold structures residing in both semantic representations and visual features are not effectively incorporated into the learned projection function. Moreover, these methods may suffer from the inherent projection shift problem, due to the disjointness between seen and unseen classes. To overcome these drawbacks, we propose a novel framework termed transductive visual-semantic embedding (TVSE) for ZSL. In specific, TVSE first learns a latent embedding space to incorporate the manifold structures in both labeled source instances and unlabeled target instances under the transductive setting. In the learned space, each instance is viewed as a mixture of seen class scores. TVSE then effectively constructs the relational mapping between seen and unseen classes using the available semantic representations, and applies it to map the seen class scores of the target instances to their predictions of unseen classes. Extensive experiments on four benchmark datasets demonstrate that the proposed TVSE achieves competitive performance compared with the state-of-the-arts for zero-shot recognition and retrieval tasks.

Visual content description has been attracting broad research attention in multimedia community because it deeply uncovers intrinsic semantic facet of visual data. Most existing approaches formulate visual captioning as machine translation task (i.e., from vision to language) via a top-down paradigm with global attention, which ignores to distinguish visual and non-visual parts during word generation. In this work, we propose a novel adaptive attention strategy for visual captioning, which can selectively attend to salient visual content based on linguistic knowledge. Specifically, we design a key control unit, termed visual gate, to adaptively decide "when" and "what" the language generator attend to during the word generation process. We map all the preceding outputs of language generator into a latent space to derive the representation of sentence structures, which assists the "visual gate" to choose appropriate attention timing. Meanwhile, we employ a bottom-up workflow to learn a pool of semantic attributes for serving as the propositional attention resources. We evaluate the proposed approach on two commonly-used benchmarks, i.e., MSCOCO and MSVD. The experimental results demonstrate the superiority of our proposed approach compared to several state-of-the-art methods.

With the proliferation of e-commerce websites and the ubiquitousness of smart phones, cross-domain image retrieval using images taken by smart phones as queries to search products on e-commerce websites is emerging as a popular application. One challenge of this task is to locate the attention of both the query and database images. In particular, database images, e.g. of fashion products, on e-commerce websites are typically displayed with other accessories, and the images taken by users contain noisy background and large variations in orientation and lighting. Consequently, their attention is difficult to locate. In this paper, we exploit the rich tag information available on the e-commerce websites to locate the attention of database images. For query images, we use each candidate image in the database as the context to locate the query attention. Novel deep convolutional neural network architectures, namely TagYNet and CtxYNet, are proposed to learn the attention weights and then extract effective representations of the images. Experimental results on public datasets confirm that our approaches have significant improvement over the existing methods in terms of the retrieval accuracy and efficiency.

Recently, deep neural networks based hashing methods have greatly improved the multimedia retrieval performance by simultaneously learning feature representations and binary hash functions. Inspired by the latest advance in the asymmetric hashing scheme, in this work, we propose a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised hashing. The core idea is that two deep convolutional models are jointly trained such that their output codes for a pair of images can well reveal the similarity indicated by their semantic labels. A pairwise loss is elaborately designed to preserve the pairwise similarities between images as well as incorporating the independence and balance hash code learning criteria. By taking advantage of the flexibility of asymmetric hash functions, we devise an efficient alternating algorithm to optimize the asymmetric deep hash functions and high-quality binary code jointly. Experiments on three image benchmarks show that DAPH achieves the state-of-the-art performance on large-scale image retrieval.

Cross-modal retrieval aims to enable flexible retrieval experience across different modalities (e.g., texts vs. images). The core of cross-modal retrieval research is to learn a common subspace where the items of different modalities can be directly compared to each other. In this paper, we present a novel Adversarial Cross-Modal Retrieval (ACMR) method, which seeks an effective common subspace based on adversarial learning. Adversarial learning is implemented as an interplay between two processes. The first process, a feature projector, tries to generate a modality-invariant representation in the common subspace and to confuse the other process, modality classifier, which tries to discriminate between different modalities based on the generated representation. We further impose triplet constraints on the feature projector in order to minimize the gap among the representations of all items from different modalities with same semantic labels, while maximizing the distances among semantically different images and texts. Through the joint exploitation of the above, the underlying cross-modal semantic structure of multimedia data is better preserved when this data is projected into the common subspace. Comprehensive experimental results on four widely used benchmark datasets show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods.

Subspace representations have been widely applied for videos in many tasks. In particular, the subspace-based query-by-image video retrieval (QBIVR), facing high challenges on similarity-preserving measurements and efficient retrieval schemes, urgently needs considerable research attention. In this paper, we propose a novel subspace-based QBIVR framework to enable efficient video search. We first define a new geometry-preserving distance metric to measure the image-to-video distance, which transforms the QBIVR task to be the Maximum Inner Product Search (MIPS) problem. The merit of this distance metric lies in that it helps to preserve the genuine geometric relationship between query images and database videos to the greatest extent. To boost the efficiency of solving the MIPS problem, we introduce two asymmetric hashing schemes which can bridge the domain gap of images and videos properly. The first approach, termed Inner-product Binary Coding (IBC), achieves high-quality binary codes by learning the binary codes and coding functions simultaneously without continuous relaxations. The other one, Bilinear Binary Coding (BBC) approach, employs compact bilinear projections instead of a single large projection matrix to further improve the retrieval efficiency. Extensive experiments on four real-world video datasets verify the effectiveness of our proposed approaches, as compared to the state-of-the-art methods.

Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scenic spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. Specifically, we devise an effective hierarchy to comprehensively describe POI by integrating various types of media information (e.g., image and text) from multiple social sources. For each individual POI, we simultaneously inject semantic knowledge as well as multi-clue representative power. We collect a multi-source POI dataset from four widely-used tourism platforms. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions' popularity as compared to several baselines.

Social media has become one of the most credible sources for delivering messages, breaking news, as well as events. Predicting the future dynamics of an event at a very early stage is significantly valuable, e.g, helping company anticipate marketing trends before the event becomes mature. However, this prediction is non-trivial because a) social events always stay with "noise'' under the same topic and b) the information obtained at its early stage is too sparse and limited to support an accurate prediction. In order to overcome these two problems, in this paper, we design an event early embedding model (EEEM) that can 1) extract social events from noise, 2) find the previous similar events, and 3) predict future dynamics of a new event. Extensive experiments conducted on a large-scale dataset of Twitter data demonstrate the capacity of our model on extract events and the promising performance of prediction by considering both volume information as well as content information.

This paper proposes a generic formulation that significantly expedites the training and deployment of image classification models, particularly under the scenarios of many image categories and high feature dimensions. As the core idea, our method represents both the images and learned classifiers using binary hash codes, which are simultaneously learned from the training data. Classifying an image thereby reduces to retrieving its nearest class codes in the Hamming space. Specifically, we formulate multiclass image classification as an optimization problem over binary variables. The optimization alternatingly proceeds over the binary classifiers and image hash codes. Profiting from the special property of binary codes, we show that the sub-problems can be efficiently solved through either a binary quadratic program (BQP) or a linear program. In particular, for attacking the BQP problem, we propose a novel bit-flipping procedure which enjoys high efficacy and a local optimality guarantee. Our formulation supports a large family of empirical loss functions and is, in specific, instantiated by exponential and linear losses. Comprehensive evaluations are conducted on several representative image benchmarks. The experiments consistently exhibit reduced computational and memory complexities of model training and deployment, without sacrificing classification accuracy.

Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large-scale cross-view retrieval tasks. Most cross-view hashing methods are developed by assuming that data from different views are well paired, e.g., text-image pairs. In real-world applications, however, this fully-paired multiview setting may not be practical. The more practical yet challenging semi-paired cross-view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi-paired cross-view retrieval, dubbed semi-paired discrete hashing (SPDH). In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi-paired data in the latent subspace, we construct the cross-view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization-based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit-by-bit manner with each bit generated with a closed-form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully-paired and semi-paired settings and the results demonstrate the superiority of SPDH over several other state-of-the-art methods in term of both accuracy and scalability.

Driven by the rapid development of Internet and digital technologies, we have witnessed the explosive growth of Web images in recent years. Seeing that labels can reflect the semantic contents of the images, automatic image annotation, which can further facilitate the procedure of image semantic indexing, retrieval, and other image management tasks, has become one of the most crucial research directions in multimedia. Most of the existing annotation methods, heavily rely on well-labeled training data (expensive to collect) and/or single view of visual features (insufficient representative power). In this paper, inspired by the promising advance of feature engineering (e.g., CNN feature and scale-invariant feature transform feature) and inexhaustible image data (associated with noisy and incomplete labels) on the Web, we propose an effective and robust scheme, termed robust multi-view semi-supervised learning (RMSL), for facilitating image annotation task. Specifically, we exploit both labeled images and unlabeled images to uncover the intrinsic data structural information. Meanwhile, to comprehensively describe an individual datum, we take advantage of the correlated and complemental information derived from multiple facets of image data (i.e., multiple views or features). We devise a robust pairwise constraint on outcomes of different views to achieve annotation consistency. Furthermore, we integrate a robust classifier learning component via ℓ2,ploss, which can provide effective noise identification power during the learning process. Finally, we devise an efficient iterative algorithm to solve the optimization problem in RMSL. We conduct comprehensive experiments on three different data sets, and the results illustrate that our proposed approach is promising for automatic image annotation.

Semantic information is important for video event detection. How to automatically discover, model, and utilize semantic information to facilitate video event detection has been a challenging problem. In this paper, we propose a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modeling from video data. Specially, different from most of the approaches based on manually pre-defined concepts, we devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts (i.e., temporal sequence relationships of static-visual concepts) in segment-level. The unified model not only enables a discriminative and descriptive representation for videos, but also alleviates error propagation problem from video representation to event modeling existing in previous methods. A max-margin framework is employed to learn the model. Extensive experiments on four challenging video event datasets, i.e., MED11, CCV, UQE50, and FCVID, have been conducted to demonstrate the effectiveness of the proposed method.

Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying correlations between heterogeneous features from different modalities. A majority of recent approaches aim at learning hash functions to preserve the pairwise similarities defined by given class labels. However, these methods fail to explicitly explore the discriminative property of class labels during hash function learning. In addition, they usually discard the discrete constraints imposed on the to-be-learned binary codes, and compromise to solve a relaxed problem with quantization to obtain the approximate binary solution. Therefore, the binary codes generated by these methods are suboptimal and less discriminative to different classes. To overcome these drawbacks, we propose a novel cross-modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints. Specifically, DCH learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes. Extensive experiments on three benchmark data sets highlight the superiority of DCH under various cross-modal scenarios and show its state-of-the-art performance.

Urban bus companies have collected a tremendous amount of travel data from passengers in the past years. In spite of great value for bus schedule optimization, these data have not been fully exploited. In this paper, we leverage hundreds of millions of bus transaction records, generated when passengers board and alight, to infer time-dependent traffic and customer demand. When the traffic and demand information are available, we build an optimal model to schedule the departure time of each bus service with the objective of minimizing the average waiting time. Experimental results show that compared with the existing bus scheduling system, our model can help reduce the waiting time by a wide margin.

Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a novel spectral clustering scheme which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Besides, we preserve the natural nonnegative characteristic of the clustering labels to enhance the interpretability of the results. Moreover, to further compensate the unreliability of the learned clustering labels, we integrate an adaptive robust module with ℓ2ploss to learn prediction function for grouping unseen data. We also show that the out-of-sample component can inject discriminative knowledge into the learning of cluster labels under certain conditions. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing clustering approaches.

Learning to hash has attracted broad research interests in recent computer vision and machine learning studies, due to its ability to accomplish efficient approximate nearest neighbor search. However, the closely related task, maximum inner product search (MIPS), has rarely been studied in this literature. To facilitate the MIPS study, in this paper, we introduce a general binary coding framework based on asymmetric hash functions, named asymmetric inner-product binary coding (AIBC). In particular, AIBC learns two different hash functions, which can reveal the inner products between original data vectors by the generated binary vectors. Although conceptually simple, the associated optimization is very challenging due to the highly nonsmooth nature of the objective that involves sign functions. We tackle the nonsmooth optimization in an alternating manner, by which each single coding function is optimized in an efficient discrete manner. We also simplify the objective by discarding the quadratic regularization term which significantly boosts the learning efficiency. Both problems are optimized in an effective discrete way without continuous relaxations, which produces high-quality hash codes. In addition, we extend the AIBC approach to the supervised hashing scenario, where the inner products of learned binary codes are forced to fit the supervised similarities. Extensive experiments on several benchmark image retrieval databases validate the superiority of the AIBC approaches over many recently proposed hashing algorithms.

Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to effectively integrate heterogeneous features from different modalities to learn hash functions using available supervising information, e.g., class labels. Existing hashing based methods generally project heterogeneous features to a common space for hash codes generation, and the supervising information is incrementally used for improving performance. However, these methods may produce ineffective hash codes, due to the failure to explore the discriminative property of supervising information and to effectively bridge the semantic gap between different modalities. To address these challenges, we propose a novel hashing based method in a linear classification framework, in which the proposed method learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective optimization algorithm is developed for the proposed method to jointly learn the modality-specific hash function, the unified binary codes and a linear classifier. Extensive experiments on three benchmark datasets highlight the advantage of the proposed method and show that it achieves the state-of-the-art performance.

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge (\emph{e.g.}, semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels (i.e., 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.

Video captioning has been attracting broad research attention in multimedia community. However, most existing approaches either ignore temporal information among video frames or just employ local contextual temporal knowledge. In this work, we propose a novel video captioning framework, termed as \emph{Bidirectional Long-Short Term Memory} (BiLSTM), which deeply captures bidirectional global temporal structure in video. Specifically, we first devise a joint visual modelling approach to encode video data by combining a forward LSTM pass, a backward LSTM pass, together with visual features from Convolutional Neural Networks (CNNs). Then, we inject the derived video representation into the subsequent language model for initialization. The benefits are in two folds: 1) comprehensively preserving sequential and visual information; and 2) adaptively learning dense visual features and sparse semantic representations for videos and sentences, respectively. We verify the effectiveness of our proposed video captioning framework on a commonly-used benchmark, i.e., Microsoft Video Description (MSVD) corpus, and the experimental results demonstrate that the superiority of the proposed approach as compared to several state-of-the-art methods.

Cross-modal retrieval has been attracting increasing attention because of the explosion of multi-modal data, e.g., texts and images. Most supervised cross-modal retrieval methods learn discriminant common subspaces minimizing the heterogeneity of different modalities by exploiting the label information. However, these methods neglect the fact that, in practice, the given labels of training data might be incomplete (i.e., some of their labels are missing). The low-quality labels result in less effective subspace and consequent unsatisfactory retrieval performance. To tackle this, we propose a novel model that simultaneously performs label completion and cross-modal retrieval. Specifically, we assume the to-be-learned common subspace can be jointly derived through two aspects: 1) linear projection from modality-specific features and 2) enriching mapping from the incomplete labels. We thus formulate the subspace learning problem as a co-regularized learning framework based on multi-modal features and incomplete labels. Extensive experiments on two large-scale multi-modal datasets demonstrate the superiority of our model for both label completion and cross-modal retrieval over the state-of-the-arts.

Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision, and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this paper, we propose a novel binary code optimization method, dubbed discrete proximal linearized minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this paper by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised ℓ2loss encodes the whole NUS-WIDE database into 64-b binary codes within 10 s on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale data sets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

In recent years, the task of event recognition from videos has attracted increasing interest in multimedia area. While most of the existing research was mainly focused on exploring visual cues to handle relatively small-granular events, it is difficult to directly analyze video content without any prior knowledge. Therefore, synthesizing both the visual and semantic analysis is a natural way for video event understanding. In this paper, we study the problem of Web video event recognition, where Web videos often describe large-granular events and carry limited textual information. Key challenges include how to accurately represent event semantics from incomplete textual information and how to effectively explore the correlation between visual and textual cues for video event understanding. We propose a novel framework to perform complex event recognition from Web videos. In order to compensate the insufficient expressive power of visual cues, we construct an event knowledge base by deeply mining semantic information from ubiquitous Web documents. This event knowledge base is capable of describing each event with comprehensive semantics. By utilizing this base, the textual cues for a video can be significantly enriched. Furthermore, we introduce a two-view adaptive regression model, which explores the intrinsic correlation between the visual and textual cues of the videos to learn reliable classifiers. Extensive experiments on two real-world video data sets show the effectiveness of our proposed framework and prove that the event knowledge base indeed helps improve the performance of Web video event recognition.

Fine-grained image categorization is a challenging task aiming at distinguishing objects belonging to the same basic-level category, e.g., leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and so on. Most of the existing methods either have difficulties to detect discriminative object components automatically, or suffer from the limited amount of training data in each sub-category. To solve these problems, this paper proposes a new fine-grained image categorization model. The key is a dense graph mining algorithm that hierarchically localizes discriminative object parts in each image. More specifically, to mimic the human hierarchical perception mechanism, a superpixel pyramid is generated for each image. Thereby, graphlets from each layer are constructed to seamlessly capture object components. Intuitively, graphlets representative to each super-/sub-category is densely distributed in their feature space. Thus, a dense graph mining algorithm is developed to discover graphlets representative to each super-/sub-category. Finally, the discovered graphlets from pairwise images are integrated into an image kernel for fine-grained recognition. Theoretically, the learned kernel can generalize several state-of-the-art image kernels. Experiments on nine image sets demonstrate the advantage of our method. Moreover, the discovered graphlets from each sub-category accurately capture those tiny discriminative object components, e.g., bird claws, heads, and bodies.

We present a deep learning strategy to fuse multiple semantic cues for complex event recognition. In particular, we tackle the recognition task by answering how to jointly analyze human actions (who is doing what), objects (what), and scenes (where). First, each type of semantic features (e.g., human action trajectories) is fed into a corresponding multi-layer feature abstraction pathway, followed by a fusion layer connecting all the different pathways. Second, the correlations of how the semantic cues interacting with each other are learned in an unsupervised cross-modality autoencoder fashion. Finally, by fine-tuning a large-margin objective deployed on this deep architecture, we are able to answer the question on how the semantic cues of who, what, and where compose a complex event. As compared with the traditional feature fusion methods (e.g., various early or late strategies), our method jointly learns the essential higher level features that are most effective for fusion and recognition. We perform extensive experiments on two real-world complex event video benchmarks, MED'11 and CCV, and demonstrate that our method outperforms the best published results by 21% and 11%, respectively, on an event recognition task.

Image location prediction is used to estimate the geolocation where an image is taken, which is important for many image applications, such as image retrieval, image browsing, and organization. Since a social image contains heterogeneous contents, such as visual content and textual content, effectively incorporating these contents to predict location is nontrivial. Moreover, it is observed that image content patterns and the locations where they may appear correlate hierarchically. Traditional image location prediction methods mainly adopt a single-level architecture and assume images are independently distributed in geographical space, which is not directly adaptable to the hierarchical correlation. In this paper, we propose a geographically hierarchical bi-modal deep belief network (GH-BDBN) model, which is a compositional learning architecture that integrates multi-modal deep learning model with a non-parametric hierarchical prior model. GH-BDBN learns a joint representation capturing the correlations among different types of image content using a bi-modal DBN, with a geographically hierarchical prior over the joint representation to model the hierarchical correlation between image content and location. Then, an efficient inference algorithm is proposed to learn the parameters and the geographical hierarchical structure of geographical locations. Experimental results demonstrate the superiority of our model for image location prediction.

Binary coding or hashing techniques are recognized to accomplish efficient near neighbor search, and have thus attracted broad interests in the recent vision and learning studies. However, such studies have rarely been dedicated to Maximum Inner Product Search (MIPS), which plays a critical role in various vision applications. In this paper, we investigate learning binary codes to exclusively handle the MIPS problem. Inspired by the latest advance in asymmetric hashing schemes, we propose an asymmetric binary code learning framework based on inner product fitting. Specifically, two sets of coding functions are learned such that the inner products between their generated binary codes can reveal the inner products between original data vectors. We also propose an alternative simpler objective which maximizes the correlations between the inner products of the produced binary codes and raw data vectors. In both objectives, the binary codes and coding functions are simultaneously learned without continuous relaxations, which is the key to achieving high-quality binary codes. We evaluate the proposed method, dubbed Asymmetric Inner-product Binary Coding (AIBC), relying on the two objectives on several large-scale image datasets. Both of them are superior to the state-of-the-art binary coding and hashing methods in performing MIPS tasks.

The vast amount of geo-tagged social images has attracted great attention in research of predicting location using the plentiful content of images, such as visual content and textual description. Most of the existing researches use the text-based or vision-based method to predict location. There still exists a problem: how to effectively exploit the correlation between different types of content as well as their geographical distributions for location prediction. In this paper, we propose to predict image location by learning the latent relation between geographical location and multiple types of image content. In particularly, we propose a geographical topic model GTMSI (geographical topic model of social image) to integrate multiple types of image content as well as the geographical distributions. In GTMI, image topic is modeled on both text vocabulary and visual feature. Each region has its own distribution over topics and hence has its own language model and vision pattern. The location of a new image is estimated based on the joint probability of image content and similarity measure on topic distribution between images. Experiment results demonstrate the performance of location prediction based on GTMSI.

As one of the most representative social media platforms, Twitter provides various real-life information on social events in real time. Despite that social event detection has been actively studied, tweet images, which appear in around 36 percent of the total tweets, have not been well utilized for this research problem. Most existing event detection methods tend to represent an image as a bag-of-visual-words and then process these visual words in the same way as textual words. This may not fully exploit the visual properties of images. State-of-the-art visual features like convolutional neural network (CNN) features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image's semantics. Unfortunately, they have not been employed in detecting events from social websites. Hence, how to make the most of tweet images to improve the performance of social event detection and visualization remains open. In this paper, we thoroughly study the impact of tweet images on social event detection for different event categories using various visual features. A novel topic model which jointly models five Twitter features (text, image, location, timestamp and hashtag) is designed to discover events from the sheer amount of tweets. Moreover, the evolutions of events are tracked by linking the events detected on adjacent days and each event is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

Nowadays the locations of social images play an important role in geographic knowledge discovery. However, most social images still lack the location information, driving location estimation for social images to have recently become an active research topic. With the rapid growth of social images, new challenges have been posed: 1) data quality of social images is an issue because they are often associated with noises and error-prone user-generated content, such as junk comments and misspelled words; and 2) data sparsity exists in social images despite the large volume, since most of them are unevenly distributed around the world and their contextual information is often missing or incomplete. In this paper, we propose a spatial-aware multimodal location estimation (SMLE) framework to tackle the above challenges. Specifically, a spatial-aware language model (SLM) is proposed to detect the high quality location-indicative tags from large datasets. We also design a spatial-aware topic model, namely spatial-aware regularized latent semantic indexing (SRLSI), to discover geographic topics and alleviate the data sparseness problem existing in language modeling. Taking multi-modalities of social images into consideration, we employ the learning to rank approach to fuse multiple evidences derived from textual features represented by SLM and SRLSI, and visual features represented by bag-of-visual-words (BoVW). Importantly, an ad hoc method is introduced to construct the training dataset with spatial-aware relevance labels for learning to rank training. Finally, given a query image, its location is estimated as the location of its most relevant image returned from the learning to rank model. The proposed framework is evaluated on a public benchmark provided by MediaEval 2013 Placing Task, which contains more than 8.5 million images crawled from Flickr. Extensive experiments on this dataset demonstrate the superior performance of the proposed methods over the state-of-the-art approaches.

As positioning data and other sensor information such as orientation measurement became powerful contextual features generated by mobile devices during video recording, a model capturing geographic field-of-view (FOV) has been developed for georeferenced video search. The accurate representation of an FOV is through the geometric shape of a circular sector. However, previous work simply employed a rectilinear vector model to represent the coverage area of a video scene. In this study, we propose to use a novel circular sector model with beginning-ending vectors for FOV representation which additionally explores viewable angle information. Its major advantage is that it leads to a more accurate georeferenced video search without false positives or false negatives (which occur in previous model using single vector). We demonstrate how our model can be applied to perform different types of overlap queries for spatial data selection in a unified framework, while providing competitive performance in terms of efficiency.

With the explosive increasing of web image data, image annotation has become a critical research issue for image semantic index and search. In this work, we propose a novel model, termed as multi-view semi-supervised learning (MVSSL), for robust image annotation task. Specifically, we exploit both labeled images and unlabeled images to uncover the intrinsic data structural information. Meanwhile, to comprehensively describe an individual datum, we take advantage of the correlated and complemental information derived from multiple facets of image data (i.e., multiple views or features). We devise a robust pair-wise constraint on outcomes of different views to achieve annotation consistency. Furthermore, we integrate a robust classifier learning component via l2,1 loss, which can provide effective noise identification power during the learning process. Finally, we devise an efficient iterative algorithm to solve the optimization problem in MVSSL. We conduct extensive experiments on the NUS-WIDE dataset, and the results illustrate that our proposed approach is promising for large scale web image annotation task.

Nowadays massive amount of images and texts has been emerging on the Internet, arousing the demand of effective cross-modal retrieval such as text-to-image search and image-to-text search. To eliminate the heterogeneity between the modalities of images and texts, the existing subspace learning methods try to learn a common latent subspace under which cross-modal matching can be performed. However, these methods usually require fully paired samples (images with corresponding texts) and also ignore the class label information along with the paired samples. This may inhibit these methods from learning an effective subspace since the correlations between two modalities are implicitly incorporated. Indeed, the class label information can reduce the semantic gap between different modalities and explicitly guide the subspace learning procedure. In addition, the large quantities of unpaired samples (images or texts) may provide useful side information to enrich the representations from learned subspace. Thus, in this paper we propose a novel model for cross-modal retrieval problem. It consists of 1) a semi-supervised coupled dictionary learning step to generate homogeneously sparse representations for different modalities based on both paired and unpaired samples; 2) a coupled feature mapping step to project the sparse representations of different modalities into a common subspace defined by class label information to perform cross-modal matching. Experiments on a large scale web image dataset MIRFlickr-1M with both fully paired and unpaired settings show the effectiveness of the proposed model on the cross-modal retrieval task.

In recent years, tremendous research endeavours have been dedicated to seeking effective visual representations for facilitating various multimedia applications, such as visual annotation and retrieval. Nonetheless, existing approaches can hardly achieve satisfactory performance due to the scarcity of fully exploring semantic properties of visual codes. In this paper, we present a novel visual coding approach, termed as hierarchical semantic visual coding (HSVC), to effectively encode visual objects (e.g., image and video) in a semantic hierarchy. Specifically, we first construct a semantic-enriched dictionary hierarchy, which is comprised of dictionaries corresponding to all concepts in a semantic hierarchy as well as their hierarchical semantic correlation. Moreover, we devise an on-line semantic coding model, which simultaneously 1) exploits the rich hierarchical semantic prior knowledge in the learned dictionary, 2) reflects semantic sparse property of visual codes, and 3) explores semantic relationships among concepts in the semantic hierarchy. To this end, we propose to integrate concept-level group sparsity constraint and semantic correlation matrix into a unified regularization term. We design an effective algorithm to optimize the proposed model, and a rigorous mathematical analysis has been provided to guarantee that the algorithm converges to a global optima. Extensive experiments on various multimedia datasets have been conducted to illustrate the superiority of our proposed approach as compared to state-of-the-art methods.

Feature representation for multimedia content is the key to the progress of many fundamental multimedia tasks. Although recent advances in deep feature learning offer a promising route towards these tasks, they are limited in application to domains where high-quality and large-scale training data are hard to obtain. In this paper, we propose a novel deep feature learning paradigm based on large, noisy and social image-tag collections, which can be acquired from the inexhaustible social multimedia content on the Web. Instead of learning features from high-quality image-label supervision, we propose to learn from the image-word semantic relations, in a way of seeking a unified image-word embedding space, where the pairwise feature similarities preserve the semantic relations in the original image-word pairs. We offer an easy-to-use implementation for the proposed paradigm, which is fast and compatible for integrating into any state-of-the-art deep architectures. Experiments on NUSWIDE benchmark demonstrate that the features learned by our method significantly outperforms other state-of-the-art ones.

In this paper, we investigate how to establish the relationship between semantic concepts based on the large-scale realworld click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc. We first define five specific relationships between concepts. We then extract some concept relationship features in textual and visual domain to train the concept relationship models. The relationship of each pair of concepts will thus be classified into one of the five special relationships. We study the efficacy of the conceptual relationships by applying them to augment imperfect image tags, i.e., improve representative power. We further employ a sophisticated hashing approach to transform augmented image tags into binary codes, which are subsequently used for content-based image retrieval task. Experimental results on NUS-WIDE dataset demonstrate the superiority of our proposed approach as compared to state-of-the-art methods.

In big data era, the ever-increasing image data has posed significant challenge on modern image retrieval. It is of great importance to index images with semantic keywords efficiently and effectively, especially confronted with fast-evolving property of the web. Learning-based hashing has shown its power in handling large-scale high-dimensional applications, such as image retrieval. Existing solutions normally separate the process of learning binary codes and hash functions into two independent stages to bypass challenge of the discrete constraints on binary codes. In this work, we propose a novel unsupervised hashing approach, namely robust discrete hashing (RDSH), to facilitate large-scale semantic indexing of image data. Specifically, RDSH simultaneously learns discrete binary codes as well as robust hash functions within a unified model. In order to suppress the influence of unreliable binary codes and learn robust hash functions, we also integrate a flexible `2;p loss with nonlinear kernel embedding to adapt to different noise levels. Finally, we devise an alternating algorithm to efficiently optimize RDSH model. Given a test image, we first conduct r-nearest-neighbor search based on Hamming distance of binary codes, and then propagate semantic keywords of neighbors to the test image. Extensive experiments have been conducted on various real-world image datasets to show its superiority to the state-of-the-arts in large-scale semantic indexing.

Clustering, as one of the most classical research problems in pattern recognition and data mining, has been widely explored and applied to various applications. Due to the rapid evolution of data on the Web, more emerging challenges have been posed on traditional clustering techniques: 1) correlations among related clustering tasks and/or within individual task are not well captured; 2) the problem of clustering out-of-sample data is seldom considered; and 3) the discriminative property of cluster label matrix is not well explored. In this paper, we propose a novel clustering model, namely multitask spectral clustering (MTSC), to cope with the above challenges. Specifically, two types of correlations are well considered: 1) intertask clustering correlation, which refers the relations among different clustering tasks and 2) intratask learning correlation, which enables the processes of learning cluster labels and learning mapping function to reinforce each other. We incorporate a novel l2,p -norm regularizer to control the coherence of all the tasks based on an assumption that related tasks should share a common low-dimensional representation. Moreover, for each individual task, an explicit mapping function is simultaneously learnt for predicting cluster labels by mapping features to the cluster label matrix. Meanwhile, we show that the learning process can naturally incorporate discriminative information to further improve clustering performance. We explore and discuss the relationships between our proposed model and several representative clustering techniques, including spectral clustering, k -means and discriminative k -means. Extensive experiments on various real-world datasets illustrate the advantage of the proposed MTSC model compared to state-of-the-art clustering approaches.

The availability of massive RGB-depth (RGB-D) images poses a compelling need for effective RGB-D content understanding techniques. RGB-D images provide synchronized information from multiple views (e.g., color and depth) of real-world objects and scenes. This work proposes learning compact and discriminative features from the multiple views of RGB-D content toward effective feature representation for RGB-D image understanding. In particular, a robust multiview feature learning approach is developed, which exploits the intrinsic relations among multiple views. The feature learning in multiple views is jointly optimized in an integrated formulation. The joint optimization essentially exploits the intrinsic relations among the views, leading to effective features and making the learning process robust to noises. The feature learning function is formulated as a robust nonnegative graph embedding function over multiple graphs in various views. The graphs characterize the local geometric and discriminating structure of the multiview data. The joint sparsity in ℓ 1 -norm graph embedding and ℓ 21 -norm data factorization further enhances the robustness of feature learning. We derive an efficient computational solution for the proposed approach and provide rigorous theoretical proof with regard to its convergence. We apply the proposed approach to two RGB-D image understanding tasks: RGB-D object classification and RGB-D scene categorization. We conduct extensive experiments on two real-world RGB-D image datasets. The experimental results have demonstrated the effectiveness of the proposed approach.

Microblogging services have revolutionized the way people exchange information. Confronted with the ever-increasing numbers of social events and the corresponding microblogs with multimedia contents, it is desirable to provide visualized summaries to help users to quickly grasp the essence of these social events for better understanding. While existing approaches mostly focus only on text-based summary, microblog summarization with multiple media types (e.g., text, image, and video) is scarcely explored. In this paper, we propose a multimedia social event summarization framework to automatically generate visualized summaries from the microblog stream of multiple media types. Specifically, the proposed framework comprises three stages, as follows. 1) A noise removal approach is first devised to eliminate potentially noisy images. An effective spectral filtering model is exploited to estimate the probability that an image is relevant to a given event. 2) A novel cross-media probabilistic model, termed Cross-Media-LDA (CMLDA), is proposed to jointly discover subevents from microblogs of multiple media types. The intrinsic correlations among these different media types are well explored and exploited for reinforcing the cross-media subevent discovery process. 3) Finally, based on the cross-media knowledge of all the discovered subevents, a multimedia microblog summary generation process is designed to jointly identify both representative textual and visual samples, which are further aggregated to form a holistic visualized summary. We conduct extensive experiments on two real-world microblog datasets to demonstrate the superiority of the proposed framework as compared to the state-of-the-art approaches.

The use of geo tags in recording the location at which a picture was taken is becoming part of image metadata . Therefore , studying approaches to image classification that can favorably exploit both geo tags and the underlying geo context has become an emerging topic. This paper contributes to geo-aware image classification by studying how to encode geo information into image representation. Given a geo-tagged image, we propose to extract geo-aware tag features by tag propagation from the geo and visual neighbors of the given image. Depending on what neighbors are used and how they are weighted, we present and compare eight variants of geo-aware tag features. Using millions of Flickr images as source data for tag feature extraction, experiments on a popular benchmark set justify the effectiveness and robustness of the proposed tag features for geo-aware image classification.

In the above paper [ibid, vol. 16, no. 6, p. 1679, Oct. 2014], the sentence "Yang et al. [?] integrated semisupervised learning and transfer learning techniques to exploit manually-labeled images for video tagging" should have appeared as "Yang et al. [34] integrated semisupervised learning and transfer learning techniques to exploit manually-labeled images for video tagging."

The task of recognizing events from video has attracted a lot of attention in recent years. However, due to the complex nature of user-defined events, the use of purely audio- visual content analysis without domain knowledge has been found to be grossly inadequate. In this paper, we propose to construct a semantic-visual knowledge base to encode the rich event-centric concepts and their relationships from the well- established lexical databases, including FrameNet, as well as the concept-specific visual knowledge from ImageNet. Based on this semantic-visual knowledge bases, we design an effective system for video event recognition. Specifically, in order to narrow the semantic gap between the high-level complex events and low-level visual representations, we utilize the event-centric semantic concepts encoded in the knowledge base as the intermediate-level event representation, which offers both human-perceivable and machine-interpretable semantic clues for event recognition. In addition, in order to leverage the abundant ImageNet images, we propose a robust transfer learning model to learn the noise- resistant concept classifiers for videos. Extensive experiments on various real-world video datasets demonstrate the superiority of our proposed system as compared to the state-of-the-art approaches.

Image fingerprinting is regarded as an alternative approach to watermarking in terms of near-duplicate detection application. It consists of feature extraction and feature indexing. Generally, the former is mainly related to discrimination, robustness , and security while the latter closely focuses on the efficiency of fingerprints search. To enable fast fingerprints searching over a very large database, we propose a new kernelized multiple feature hashing method to convert the real-value fingerprints into compact binary-value fingerprints. During the process of converting, the proposed hashing method jointly utilizes the kernel trick and multiple feature fusion strategy to map the image represented by multiple features into a compact binary code. With the help of the kernel function, the hashing method can be applied to any format (such as string, graph, set, and so on) as long as there is an associated kernel function available for similarity measurement. In addition, taking multiple features into account aims at improving the discriminability since these multiple evidences are complementary to each other. The extensive experimental results show that the proposed algorithm outperforms state-of-the-art kernelized hashing methods by up to 10 percent.

With the rapid development of GPS-enabled mobile devices, huge amounts of user-contributed data with location information can be collected from the Internet. With this kind of data, one promising application is travel recommendation, which has attracted a considerable number of researches recently. However, most of the previous studies only focus on one aspect of the relations among users and locations or make a coarse linear combination of the relations. Moreover, all the existing work on travel recommendation do not consider recommendation to groups, which is an important characteristic of travelers' behavior. In this paper, we present a personalized travel recommendation system named Where to Go. The novelty of the system is a 3R model which can unify user-location relation, user-user relation and location-location relation into a single framework and perform random walk with restart to analyze the model. We further extend our approach to provide recommendations for groups. To the best of our knowledge, this is the first work to use random walk with restart for group recommendation. We conduct a comprehensive performance evaluation using a real dataset collected from Flickr, which is one of the most popular online photo-sharing sites. Experimental results show that our approach provides significantly superior recommendation quality compared to other state-of-the-art travel recommendation approaches for both individuals and groups.

Image tagging, also known as image annotation and image conception detection, has been extensively studied in the literature. However, most existing approaches can hardly achieve satisfactory performance owing to the deficiency and unreliability of the manually-labeled training data. In this paper, we propose a new image tagging scheme, termed social assisted media tagging (SAMT), which leverages the abundant user-generated images and the associated tags as the "social assistance" to learn the classifiers. We focus on addressing the following major challenges: (a) the noisy tags associated to the web images; and (b) the desirable robustness of the tagging model. We present a joint image tagging framework which simultaneously refines the erroneous tags of the web images as well as learns the reliable image classifiers. In particular, we devise a novel tag refinement module for identifying and eliminating the noisy tags by substantially exploring and preserving the low-rank nature of the tag matrix and the structured sparse property of the tag errors. We develop a robust image tagging module based on the l2,p-norm for learning the reliable image classifiers. The correlation of the two modules is well explored within the joint framework to reinforce each other. Extensive experiments on two real-world social image databases illustrate the superiority of the proposed approach as compared to the existing methods.

With the rapid growth in popularity of social websites, social event detection has become one of the hottest research topics. However, continuously monitoring social events has not been well studied. In this demo, we present a novel system called EventEye to effectively monitor evolving events and visualize their evolving paths, which are discovered from tweet streams. In particular, four event operations are defined for our proposed stream clustering algorithm to capture the evolutions over time and a multi-layer indexing structure is designed to support efficient event search from large-scale event databases. In our system, events are visualized in different views, including evolution graph, timeline, map view, etc.

Social Curation Service (SCS) is a new type of emerging social media platform, where users can select, organize and keep track of multimedia contents they like. In this paper, we take advantage of this great opportunity and target at the very starting point in social media: user profiling, which supports fundamental applications such as personalized search and recommendation. As compared to other profiling methods in conventional Social Network Services (SNS), our work benefits from the two distinguishable characteristics of SCS: a) organized multimedia user-generated contents, and b) content-centric social network. Based on these two characteristics, we are able to deploy the state-of-the-art multimedia analysis techniques to establish content-based user profiles by extracting user preferences and their social relations. First, we automatically construct a content-based user preference ontology and learn the ontological models to generate comprehensive user profiles. In particular, we propose a new deep learning strategy called multi-task convolutional neural network (mtCNN) to learn profile models and profile-related visual features simultaneously. Second, we propose to model the multi-level social relations offered by SCS to refine the user profiles in a low-rank recovery framework. To the best of our knowledge, our work is the first that explores how social curation can help in content-based social media technologies, taking user profiling as an example. Extensive experiments on 1,293 users and 1.5 million images collected from Pinterest in fashion domain demonstrate that recommendation methods based on the proposed user profiles are considerably more effective than other state-of-the-art recommendation strategies.

The explosive growth of video content on the Web has been revolutionizing the way people share, exchange and perceive information. At the same time, however, the massive amount of Web video content may degrade user experience in that it is tedious and time-consuming to view relevant videos uploaded by different users and grasp the gist of their content. In this demonstration, we demonstrate a system that takes Web videos related to a query event as the input and automatically generates a short mashup video that can well describe the essence of the query. Specifically, we first identify shot cliques composed of near-duplicate shots and infer their semantic meanings by resorting to relevant Web images. Then a shot clique selection algorithm is performed to select representative content according to the twofold importance of the shot cliques evaluated, namely content importance and semantic importance, with content diversity considered. Finally, the selected shot cliques are organized into a mashup video based on semantic and temporal cues.

Higher-level semantics such as visual attributes are crucial for fundamental multimedia applications. We present a novel attribute discovery approach that can automatically identify, model and name attributes from an arbitrary set of image and text pairs that can be easily gathered on the Web. Different from conventional attribute discovery methods, our approach does not rely on any pre-defined vocabularies and human labeling. Therefore, we are able to build a large visual knowledge base without any human efforts. The discovery is based on a novel deep architecture, named Independent Component Multimodal Autoencoder (ICMAE), that can continually learn shared higher-level representations across the visual and textual modalities. With the help of the resultant representations encoding strong visual and semantic evidences, we propose to (a) identify attributes and their corresponding high-quality training images, (b) iteratively model them with maximum compactness and comprehensiveness, and (c) name the attribute models with human understandable words. To date, the proposed system has discovered 1,898 attributes over 1.3 million pairs of image and text. Extensive experiments on various real-world multimedia datasets demonstrate the quality and effectiveness of the discovered attributes, facilitating multimedia applications such as image annotation and retrieval as compared to the state-of-the-art approaches.

Microblogging services have emerged as an essential way to strengthen the communications among individuals. One of the most important features of microblog over traditional social networks is the extensive proliferation in information diffusion. As the outbreak of information diffusion often brings in valuable opportunities or devastating effects, it will be beneficial if a mechanism can be provided to predict whether a piece of information will become viral, and which part of the network will participate in propagating this information. In this work, we define three types of influences, namely, interest-oriented influence, social-oriented influence, and epidemic-oriented influence, that will affect a user's decision on whether to perform a diffusion action. We propose a diffusion-targeted influence model to differentiate and quantify various types of influence. Further we model the problem of diffusion prediction by factorizing a user's intention to transmit a microblog into these influences. The learned prediction model is then used to predict the future diffusion state of any new microblog. We conduct experiments on a real-world microblogging dataset to evaluate our method, and the results demonstrate the superiority of the proposed framework as compared to the state-of-the-art approaches.

Similarity search plays an important role in many applications involving high-dimensional data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional data points into binary hash codes by effectively utilizing local structural information. In RHLM, for each individual data point in the training dataset, a local hashing model is learned and used to predict the hash codes of its neighboring data points. The local models from all the data points are globally aligned so that an optimal hash code can be assigned to each data point. After obtaining the hash codes of all the training data points, we design a robust method by employing l2,1 -norm minimization on the loss function to learn effective hash functions, which are then used to map each database point into its hash code. Given a query data point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.

Nonnegative matrix factorization (NMF) has received considerable attention in image processing, computer vision, and patter recognition. An important variant of NMF is nonnegative graph embedding (NGE), which encodes the statistical or geometric information of data in the process of matrix factorization. The NGE offers a general framework for unsupervised/supervised settings. However, NGE-like algorithms often suffer from noisy data, unreliable graphs, and noisy labels, which are commonly encountered in real-world applications. To address these issues, in this paper, we first propose a robust nonnegative graph embedding (RNGE) framework, where the joint sparsity in both graph embedding and data reconstruction endues robustness to undesirable noises. Next, we present a robust seminonnegative graph embedding (RsNGE) framework, which only constrains the coefficient matrix to be nonnegative while places no constraint on the base matrix. This extends the applicable range of RNGE to data which are not nonnegative and endows more discriminative power of the learnt base matrix. The RNGE/RsNGE provides a general formulation such that all the algorithms unified within the graph embedding framework can be easily extended to obtain their robust nonnegative/seminonnegative solutions. Further, we develop elegant multiplicative updating solutions that can solve RNGE/RsNGE efficiently and offer a rigorous convergence analysis. We conduct extensive experiments on four real-world data sets and compare the proposed RNGE/RsNGE to other representative NMF variants and data factorization methods. The experimental results demonstrate the robustness and effectiveness of the proposed approaches.

We propose a novel approach to analyze how a popular video is propagated in the cyberspace, to identify if it originated from a certain sharing-site, and to identify how it reached the current popularity in its propagation. In addition, we also estimate their influences across different websites outside the major hosting website. Web video is gaining significance due to its rich and eye-ball grabbing content. This phenomenon is evidently amplified and accelerated by the advance of Web 2.0. When a video receives some degree of popularity, it tends to appear on various websites including not only video-sharing websites but also news websites, social networks or even Wikipedia. Numerous video-sharing websites have hosted videos that reached a phenomenal level of visibility and popularity in the entire cyberspace. As a result, it is becoming more difficult to determine how the propagation took place - was the video a piece of original work that was intentionally uploaded to its major hosting site by the authors, or did the video originate from some small site then reached the sharing site after already getting a good level of popularity, or did it originate from other places in the cyberspace but the sharing site made it popular. Existing study regarding this flow of influence is lacking. Literature that discuss the problem of estimating a video's influence in the whole cyberspace also remains rare. In this article we introduce a novel framework to identify the propagation of popular videos from its major hosting site's perspective, and to estimate its influence. We define a Unified Virtual Community Space (UVCS) to model the propagation and influence of a video, and devise a novel learning method called Noise-reductive Local-and-Global Learning (NLGL) to effectively estimate a video's origin and influence. Without losing generality, we conduct experiments on annotated dataset collected from a major video sharing site to evaluate the effectiveness of the framework. Surrounding the collected videos and their ranks, some interesting discussions regarding the propagation and influence of videos as well as user behavior are also presented.

Semantic video indexing, also known as video annotation or video concept detection in literatures, has been attracting significant attention in recent years. Due to deficiency of labeled training videos, most of the existing approaches can hardly achieve satisfactory performance. In this paper, we propose a novel semantic video indexing approach, which exploits the abundant user-tagged Web images to help learn robust semantic video indexing classifiers. The following two major challenges are well studied: 1) noisy Web images with imprecise and/or incomplete tags; and 2) domain difference between images and videos. Specifically, we first apply a non-parametric approach to estimate the probabilities of images being correctly tagged as confidence scores. We then develop a robust transfer video indexing (RTVI) model to learn reliable classifiers from a limited number of training videos together with the abundance of user-tagged images. The RTVI model is equipped with a novel sample-specific robust loss function, which employs the confidence score of a Web image as prior knowledge to suppress the influence and control the contribution of this image in the learning process. Meanwhile, the RTVI model discovers an optimal kernel space, in which the mismatch between images and videos is minimized for tackling the domain difference problem. Besides, we devise an iterative algorithm to effectively optimize the proposed RTVI model and a theoretical analysis on the convergence of the proposed algorithm is provided as well. Extensive experiments on various real-world multimedia collections demonstrate the effectiveness of the proposed robust semantic video indexing approach.

This article presents a novel attribute-augmented semantic hierarchy (A 2 SH) and demonstrates its effectiveness in bridging both the semantic and intention gaps in content-based image retrieval (CBIR). A 2 SH organizes semantic concepts into multiple semantic levels and augments each concept with a set of related attributes. The attributes are used to describe the multiple facets of the concept and act as the intermediate bridge connecting the concept and low-level visual content. An hierarchical semantic similarity function is learned to characterize the semantic similarities among images for retrieval. To better capture user search intent, a hybrid feedback mechanism is developed, which collects hybrid feedback on attributes and images. This feedback is then used to refine the search results based on A 2 SH. We use A 2 SH as a basis to develop a unified content-based image retrieval system. We conduct extensive experiments on a large-scale dataset of over one million Web images. Experimental results show that the proposed A 2 SH can characterize the semantic affinities among images accurately and can shape user search intent quickly, leading to more accurate search results as compared to state-of-the-art CBIR solutions.

Microblogging services have revolutionized the way people exchange information. Confronted with the ever-increasing numbers of microblogs with multimedia contents and trending topics, it is desirable to provide visualized summarization to help users to quickly grasp the essence of topics. While existing works mostly focus on text-based methods only, summarization of multiple media types (e.g., text and image) are scarcely explored. In this paper, we propose a multimedia microblog summarization framework to automatically generate visualized summaries for trending topics. Specifically, a novel generative probabilistic model, termed multimodal-LDA (MMLDA), is proposed to discover subtopics from microblogs by exploring the correlations among different media types. Based on the information achieved from MMLDA, a multimedia summarizer is designed to separately identify representative textual and visual samples and then form a comprehensive visualized summary. We conduct extensive experiments on a real-world Sina Weibo microblog dataset to demonstrate the superiority of our proposed method against the state-of-the-art approaches.

This paper presents a novel Attribute-augmented Semantic Hierarchy (A2 SH) and demonstrates its effectiveness in bridging both the semantic and intention gaps in Content-based Image Retrieval (CBIR). A2 SH organizes the semantic concepts into multiple semantic levels and augments each concept with a set of related attributes, which describe the multiple facets of the concept and act as the intermediate bridge connecting the concept and low-level visual content. A hierarchical semantic similarity function is learnt to characterize the semantic similarities among images for retrieval. To better capture user search intent, a hybrid feedback mechanism is developed, which collects hybrid feedbacks on attributes and images. These feedbacks are then used to refine the search results based on A2 SH. We develop a content-based image retrieval system based on the proposed A2 SH. We conduct extensive experiments on a large-scale data set of over one million Web images. Experimental results show that the proposed A2 SH can characterize the semantic affinities among images accurately and can shape user search intent precisely and quickly, leading to more accurate search results as compared to state-of-the-art CBIR solutions.

In this paper, we present a new multimedia retrieval paradigm to innovate large-scale search of heterogenous multimedia data. It is able to return results of different media types from heterogeneous data sources, e.g., using a query image to retrieve relevant text documents or images from different data sources. This utilizes the widely available data from different sources and caters for the current users' demand of receiving a result list simultaneously containing multiple types of data to obtain a comprehensive understanding of the query's results. To enable large-scale inter-media retrieval, we propose a novel inter-media hashing (IMH) model to explore the correlations among multiple media types from different data sources and tackle the scalability issue. To this end, multimedia data from heterogeneous data sources are transformed into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations. Furthermore, we integrate a linear regression model to learn hashing functions so that the hash codes for new data points can be efficiently generated. Experiments conducted on real-world large-scale multimedia datasets demonstrate the superiority of our proposed method compared with state-of-the-art techniques.

Data clustering is one of the fundamental research problems in data mining and machine learning. Most of the existing clustering methods, for example, normalized cut and (k)-means, have been suffering from the fact that their optimization processes normally lead to an NP-hard problem due to the discretization of the elements in the cluster indicator matrix. A practical way to cope with this problem is to relax this constraint to allow the elements to be continuous values. The eigenvalue decomposition can be applied to generate a continuous solution, which has to be further discretized. However, the continuous solution is probably mixing-signed. This result may cause it deviate severely from the true solution, which should be naturally nonnegative. In this paper, we propose a novel clustering algorithm, i.e., discriminative nonnegative spectral clustering, to explicitly impose an additional nonnegative constraint on the cluster indicator matrix to seek for a more interpretable solution. Moreover, we show an effective regularization term which is able to not only provide more useful discriminative information but also learn a mapping function to predict cluster labels for the out-of-sample test data. Extensive experiments on various data sets illustrate the superiority of our proposal compared to the state-of-the-art clustering algorithms.

Recent years have witnessed a great explosion of user-generated videos on the Web. In order to achieve an effective and efficient video search, it is critical for modern video search engines to associate videos with semantic keywords automatically. Most of the existing video tagging methods can hardly achieve reliable performance due to deficiency of training data. It is noticed that abundant well-tagged data are available in other relevant types of media (e.g., images). In this article, we propose a novel video tagging framework, termed as Cross-Media Tag Transfer (CMTT), which utilizes the abundance of well-tagged images to facilitate video tagging. Specifically, we build a “cross-media tunnel” to transfer knowledge from images to videos. To this end, an optimal kernel space, in which distribution distance between images and video is minimized, is found to tackle the domain-shift problem. A novel cross-media video tagging model is proposed to infer tags by exploring the intrinsic local structures of both labeled and unlabeled data, and learn reliable video classifiers. An efficient algorithm is designed to optimize the proposed model in an iterative and alternative way. Extensive experiments illustrate the superiority of our proposal compared to the state-of-the-art algorithms.

In this paper, we present a novel approach, named Robust Cross-Media Transfer (RCMT), for visual event detection in social multimedia environments. Different from most existing methods, the proposed method can directly take different types of noisy social multimedia data as input and conduct robust event detection. More specifically, we build a robust model by employing an l2,1-norm regression model featuring noise tolerance, and also manage to integrate different types of social multimedia data by minimizing the distribution difference among them. Experimental results on real-life Flickr image dataset and YouTube video dataset demonstrate the effectiveness of our proposal, compared to state-of-the-art algorithms.