Natural Language-based Egocentric Task Verification (NLETV) aims to verify the alignment between action sequences in egocentric videos and their corresponding textual descriptions. However, existing NLETV approaches are still facing two critical challenges: (1) These methods are designed for simulating environments, ignoring the domain gap between synthetic and realistic data. (2) The matching processes are regarded as a simple binary classification problem, which undermines model reliability due to evaluation bias and uncalibrated decision settings. To address these challenges, we propose a novel method termed Prototypical Evidential Learning (PEL), which can be adapted to existing NLETV approaches and boost the model generalization and mitigate prediction bias. Our method leverages prototypes to guide cross-domain alignment and evidence collection. Specifically, PEL consists of two key components: (1) Prototypical Domain Adaptation module enabling cross-domain feature alignment and intra-domain prototype preservation between synthetic and realistic domains; (2) Matching Evidence Collector module, which quantifies prediction uncertainty on the prototypical representations through evidential deep learning. It enforces the model to collect the vision-text consistency and discrepancy evidence, thus addressing the issues of biased decisions in binary classification. Extensive experiments on two public datasets demonstrate that our PEL method outperforms existing state-of-the-art NLETV methods and shows remarkable generalizability.

Egocentric Task Verification (ETV) aims to determine if the operation flows of procedural tasks in egocentric videos align with the logic of given rules. Early works adopt the video-based verification paradigm that compares a reference video to the testing video, which limits the flexibility of model deployment. Recent researches incorporate reference textual rules instead of videos, describing the operational logic with natural language, but also raises the challenges of cross-modal heterogeneity and hierarchical misalignment between the two modalities. While previous works mainly address the cross-modal heterogeneity between vision and text modalities, they inevitably suffer from two additional key challenges: (1) Existing methods are mostly developed in synthetic domains, yet have not considered the issues of synthetic-to-realistic generalization challenges in real-world applications. (2) The intricate relations between visual content and textual rule involve multiple matching correlations, indicating high-order matching interactions. To address these issues, we proposed the Generalizable Egocentric Task Verification (GETV), and construct a cross-domain ETV benchmark dataset, EgoCross. It features synthetic-to-real cross-domain evaluation, covering both synthetic datasets for training and realistic datasets for testing, across three different types of tasks. Furthermore, we also propose a novel method for this challenge, termed Cross-modal Hybrid Hypergraph Matching (CHHM), which models the logical cross-modal matching in the GETV challenge as a heterogeneous hybrid hypergraph learning process, thus addressing intrinsic multiple matching correlations. Additionally, to tackle the problems of synthetic-to-realistic generalization, we enhance the cross-modal matching process with prototype-based graph representation alignment, which effectively mitigates the cross-domain gap. Extensive experiments on the existing two ETV benchmark datasets, i.e., EgoTV and CSV-NL, and our proposed GETV dataset EgoCross, demonstrate our approach establishes new state-of-the-art performance on both intra-domain and cross-domain challenges.

Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning. Deep networks are vulnerable to such label-corrupted samples due to the memorization effect. One major stream of previous methods concentrates on identifying clean data for training. However, these methods often neglect imbalances in label noise across different mini-batches and devote insufficient attention to out-of-distribution noisy data. To this end, we propose a noise-robust method named Jo-SNC (Joint sample selection and model regularization based on Self- and Neighbor-Consistency). Specifically, we propose to employ the Jensen-Shannon divergence to measure the "likelihood" of a sample being clean or out-of-distribution. This process factors in the nearest neighbors of each sample to reinforce the reliability of clean sample identification. We design a self-adaptive, data-driven thresholding scheme to adjust per-class selection thresholds. While clean samples undergo conventional training, detected in-distribution and out-of-distribution noisy samples are trained following partial label learning and negative learning, respectively. Finally, we advance the model performance further by proposing a triplet consistency regularization that promotes self-prediction consistency, neighbor-prediction consistency, and feature consistency. Extensive experiments on various benchmark datasets and comprehensive ablation studies demonstrate the effectiveness and superiority of our approach over existing state-of-the-art methods.

Inertial Measurement Units (IMUs) can capture intricate kinematic behaviors, thereby enhancing the performance of human action recognition methods. Consequently, this technology has recently garnered considerable attention within this domain. However, existing methods either encounter limitations in instance-level visual representation due to self-occlusion or fail to fully utilize the potential of complex kinematic information, making it challenging to adequately capture the intricate relationships between the two data sources. In this paper, we tackle this issue by addressing the problem through causal learning and Fourier learning. Specifically, we introduce a novel framework calledCausal-Inspired Fourier Representation Learning (CIFRL)for Wearable IMUs and Egocentric Action Recognition, which aims to enhance cross-modal feature alignment. The framework consists of two key components: (1) Temporal Causal Modeling (TCM), designed for video interpretation from a causal perspective; (2) Spectral-Temporal Learning (STL), which aims to decompose the inertial data using Fourier representation and align cross-modal features. We evaluate our proposed framework on the WEAR and CMU-MMAC benchmarks. Empirical results demonstrate the superior performance of our CIFRL approach compared to state-of-the-art methods. Our code is available at https://github.com/Adrianos1219/CIFRL.

The grounded question answering in egocentric videos (Ego-GQA) aims to identify the relevant temporal window and generate corresponding responses in natural language given a textual question. Compared with third-person videos, egocentric video understanding requires more advanced human-centric thinking capability. However, existing Ego-GQA approaches often fail to distinguish the inherent limitations of dynamic egocentric context understanding, treating both first-person and third-person perspectives equally. This oversight leads to hallucinations and a lack of proper egocentric reasoning in first-person video understanding. To address this issue, we propose a novel Collaborated with Hallucination (CoHa) framework for the Ego-GQA, which quantifies the hallucinations generated by an Ego-GQA model and further leverages them as error demonstrations to constrain the model's reasoning process, encouraging it to ground predictions in egocentric visual cues instead of relying on biased pretraining priors. Specifically, we first employ Subjective Logic to quantify the degree of uncertainty in unreliable answers. We then generate diffusion-based noisy visual inputs to amplify the hallucinations as error demonstrations, which are used to append appropriate constraints to the model according to the uncertainty. These constraints effectively steer predictions away from the unreliable semantics induced by inherent drawbacks in egocentric thinking. Additionally, we incorporate an interactive refinement module to facilitate the model to explore more fine-grained cues observed from the first-person view. Extensive experiments on two widely used benchmarks demonstrate that our CoHa method outperforms recent state-of-the-art methods. Our code is available at https://github.com/Mrshenshen/CoHa.

Existing semi-supervised semantic segmentation (SSS) methods fail to explore the potential of depth information in unlabeled data, as they suffer from 1) inter-class depth similarity, and 2) intra-class depth discrepancy. To address these challenges, this paper proposes DepMatch, a simple yet effective approach that leverages depth difference knowledge to guide consistency learning. Specifically, a Class-wise Depth Disparity Perception (CDDP) module is designed to exploit depth difference information, driven by class prediction priors, facilitating robust feature learning. Depth-feature discrepancy set is first constructed and then reliable pixel pairs are selected for inter-class depth disparity knowledge distillation. Simultaneously, exponential normalization is applied to intra-category depth disparity for suppressing large outlier variations, and an entropy-based adaptive weight is derived to prioritize feature learning of high entropy areas. Moreover, we propose the Uncertain Logit Disparity Regulation (ULDR) module, which leverages the depth variations at class boundaries to promote the mutual regulation of uncertain pixel logit information, enhancing the model's spatial understanding. Experiments on five public benchmarks show that DepMatch can be seamlessly incorporated as a plug-and-play plugin into popular SSS frameworks, achieving significant performance improvements across various visual encoders. The source code and models are made available at https://github.com/NUST-Machine-Intelligence-Laboratory/DepMatch.

Natural Language-based Egocentric Task Verification (NLETV) aims to equip agents to determine if operation flows of procedural tasks in egocentric videos align with natural language instructions. Describing rules with natural language provides generalizable applications, but also raises cross-modal heterogeneity and hierarchical misalignment challenges. In this paper, we proposed a novel approach termed Procedural Heterogeneous Graph Completion (PHGC), which addresses these challenges with heterogeneous graphs representing the logic in rules and operation flows. Specifically, our PHGC method mainly consists of three key components: (1) Heterogeneous Graph Construction module that defines objective states and operation flows as vertices, with temporal and sequential relations as edges. (2) Cross-Modal Path Finding module that aligns semantic relations between hierarchical video and text elements. (3) Discriminative Entity Representation module excavates hidden entities that integrate general logical relations and discriminative cues to reveal final verification results. Additionally, we further constructed a new dataset called CSV-NL comprised of realistic videos. Extensive experiments on the two benchmark datasets covering both digital and physical scenarios, i.e., EgoTV and CSV-NL, demonstrate that our proposed PHGC establishes state-of-the-art performance across different settings. Our code and dataset are available at https://github.com/XunCHN/PHGC.

The Egocentric Online Action Segmentation (EOAS) task aims to sequentially segment untrimmed egocentric videos into distinct action segments in a streaming manner. Previous methods primarily focused on improving contextual information utilization, which highly relied on leveraging the prior context. However, under online constraints, the absence of post context limits the effectiveness of the prior context in learning the action semantics. Excessive reliance on prior context may lead to insufficient feature representations of current presented behavior. To tackle this problem, we propose a novel EOAS method, termed Behavior-Centred Feature Augmentation (BCFA), which consists of two key modules: (1) Behavior Prototype Learning models the common sense of each action across different surroundings, enhancing the model’s ability to capture the shared characteristics of behaviors. (2) Presented Behavior Enhancement leverages both the intrinsic characteristics of the current presented behavior itself and the common sense captured by BPL for feature enhancement, mitigating the absence of post contextualization. We evaluate our proposed BCFA method on three public EOAS benchmark datasets, GTEA, EgoProceL, and EgoPER, and demonstrate that our proposed BCFA approach outperforms recent state-of-the-art methods.

Error detection in egocentric procedural task videos aims to identify deviations to support intelligent monitoring and task automation. Despite making significant progress, existing methods that leverage prototypes for egocentric error detection have two drawbacks: (1) The neglect of inherent data traits, i.e., large intra-class variance and minimal inter-class distinction. (2) The absence of causal consistency in temporal modeling. To address these challenges, we introduce a novel framework termed Probabilistic Embeddings with Causal Constraint (PECC) for error detection in egocentric procedural videos. Specifically, we first integrated a causal dilated convolution module in temporal action segmentation model to capture temporal causal consistency. We then train Gaussian Mixture Models (GMMs) for each action class to get frame-level probabilistic embeddings. Finally, We evaluate test frames using log-likelihood values to detect erroneous actions. Extensive experiments conducted on EgoPER and HoloAssist demonstrate that our method achieves state-of-the-art performance, significantly surpassing existing methods in error detection. Our code is available at https://github.com/HouTong-s/PECC-for-Error-Detection-in-Egocentric-Videos.

Cross-Modal Task Verification (CMTV) assesses whether a procedural task is executed accurately according to language-based rules, presenting challenges due to its multi-modal and chronological nature. Existing methods using graph or neuro-symbolic approaches face two issues: (1) Conventional methods only model linear sequential relationships among intra-modal nodes, ignoring implicit relationships between non-neighboring nodes. (2) Directed graphs model pairwise cross-modal relationships but overlook cases where a step corresponds to multiple video segments. To address these issues, we propose Hypergraph-based Sequential Matching (HSM) with two components: (1) Temporal Complementary Hypergraph Module (TCHM), a hierarchical sequential hyperedge construction method that focuses on both sequential connections and implicit relationships across nodes. (2) Step-wise Hypergraph Modeling (SHM), a novel hypergraph-based alignment mechanism that better aligns an action description with multiple video segments, improving task verification accuracy. We evaluate HSM on EgoTV and CTV datasets, demonstrating its superiority over state-of-the-art methods.

Cross-domain image retrieval task is derived from traditional image retrieval task, wherein the model aims to find images in another domain that share the same semantic meaning. Existing methods first achieve the pseudo labels through clustering algorithm, and then perform the cross-domain alignment. However, in unsupervised scenarios, data lacking explicit semantic information can easily induce the model to produce erroneous predictions, which can significantly deteriorate existing clustering-based methods. To mitigate the influence of these noisy instances, we propose the Noise Mitigation (NM) method, utilizing information entropy to separate noisy instances from clear data. Moreover, our label adaption strategy can enhance the prediction accuracy of noisy data by leveraging clear data. We subsequently apply the explicit semantic maximization strategy, selectively construct an intermediate domain through fusing the explicit semantic in clear data, further reducing the influence of noisy instances. Our approach is evaluated on three datasets, and the experimental results demonstrate the overwhelming performance superiority of our noise mitigation strategy.

Multimodal Multi-label Emotion Recognition (MMER) aims to identify human emotions through various modalities. Previous studies mainly focus on aligning cross-modal data to extract discriminative emotion-dependent features using attention or reconstruction-based strategies, while omitting the fact that the MMER task is also subjected to the multi-label noises that exist in the multi-label classifications, which disturb the modality-to-label correlations. Besides, most of the research also failed to balance the strategy of finding internal label correlations and label dependency of modalities in noisy conditions. In this paper, we proposed a novel Heterogeneous Graph Embedding (HGE) method for the MMER task, which exploits heterogeneous graphs to extract emotional commonality in the modal and temporal levels, and explicitly models cross-modal correlations among heterogeneous modalities. Additionally, it also captures uncertainty brought by multi-label noise and leverages the unevenness of multi-label to overcome potential data issues. Experimental results demonstrate that our HGE method achieves state-of-the-art performance on two widely used multimodal multi-label emotion recognition datasets under both noise-free and noisy circumstances.

Event retrieval involves searching for specific events from untrimmed video galleries and has garnered significant attention in recent years. However, most existing works follow a text-based video retrieval paradigm only, limited by two main drawbacks: (1) The episodic information presented in described events is not fully perceived, leading to declines in retrieval performance facing variable query intentions. (2) Current models are prone to returning false positive results with similar semantics, as simple text queries can hardly accurately describe the target video content users seek. In this paper, we propose a novel event retrieval framework termed Composed Query-Based Event Retrieval (CQBER). Specifically, we first construct two CQBER benchmark datasets, namely ActivityNet-CQ and TVR-CQ, which cover TV shows and open-world scenarios, respectively. Additionally, we propose an initial CQBER method, termed Multimodal Episodic Perceptron (MEP), which excavates complete query semantics from both observed static visual cues and various descriptions. Extensive experiments demonstrate that our proposed framework significantly boosts event retrieval accuracy across different existing methods. Our code and datasets are available at https://github.com/VincentVanNF/CQBER.

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.

Remote sensing change detection is essential for environmental monitoring, urban planning, and related applications. However, current methods often struggle to capture long-range dependencies while maintaining computational efficiency. Although Transformers can effectively model global context, their quadratic complexity poses scalability challenges, and existing linear attention approaches frequently fail to capture intricate spatiotemporal relationships. Drawing inspiration from the recent success of Titans in language tasks, we present ChangeTitans, the Titans-based framework for remote sensing change detection. Specifically, we propose VTitans, the first Titans-based vision backbone that integrates neural memory with segmented local attention, thereby capturing long-range dependencies while mitigating computational overhead. Next, we present a hierarchical VTitans-Adapter to refine multi-scale features across different network layers. Finally, we introduce TS-CBAM, a two-stream fusion module leveraging cross-temporal attention to suppress pseudo-changes and enhance detection accuracy. Experimental evaluations on four benchmark datasets (LEVIR-CD, WHU-CD, LEVIR-CD+, and SYSU-CD) demonstrate that ChangeTitans achieves state-of-the-art results, attaining 84.36% IoU and 91.52% F1-score on LEVIR-CD, while remaining computationally competitive. Our code and model are available at https://github.com/ChangeTitans/ChangeTitans.

Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training. Some UAD applications intend to locate the anomalous regions further even without any anomaly information. Although the absence of anomalous samples and annotations deteriorates the UAD performance, an inconspicuous, yet powerful statistics model, the normalizing flows, is appropriate for anomaly detection (AD) and localization in an unsupervised fashion. The flow-based probabilistic models, only trained on anomaly-free data, can efficiently distinguish unpredictable anomalies by assigning them much lower likelihoods than normal data. Nevertheless, the size variation of unpredictable anomalies introduces another inconvenience to the flow-based methods for high-precision AD and localization. To generalize the anomaly size variation, we propose a novel multiscale flow-based framework (MSFlow) composed of asymmetrical parallel flows followed by a fusion flow to exchange multiscale perceptions. Moreover, different multiscale aggregation strategies are adopted for image-wise AD and pixel-wise anomaly localization according to the discrepancy between them. The proposed MSFlow is evaluated on three AD datasets, significantly outperforming existing methods. Notably, on the challenging MVTec AD benchmark, our MSFlow achieves a new state-of-the-art (SOTA) with a detection AUORC score of up to 99.7%, localization AUCROC score of 98.8% and PRO score of 97.1%.

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.

As a fundamental problem in multimodal learning, multimodal fusion aims to compensate for the inherent limitations of a single modality. One challenge of multimodal fusion is that the unimodal data in their unique embedding space mostly contains potential noise, which leads to corrupted cross-modal interactions. However, in this paper, we show that the potential noise in unimodal data could be well quantified and further employed to enhance more stable unimodal embeddings via contrastive learning. Specifically, we propose a novel generic and robust multimodal fusion strategy, termed Embracing Aleatoric Uncertainty (EAU), which is simple and can be applied to kinds of modalities. It consists of two key steps: (1) the Stable Unimodal Feature Augmentation (SUFA) that learns a stable unimodal representation by incorporating the aleatoric uncertainty into self-supervised contrastive learning. (2) Robust Multimodal Feature Integration (RMFI) leveraging an information-theoretic strategy to learn a robust compact joint representation. We evaluate our proposed EAU method on five multimodal datasets, where the video, RGB image, text, audio, and depth image are involved. Extensive experiments demonstrate the EAU method is more noise-resistant than existing multimodal fusion strategies and establishes new state-of-the-art on several benchmarks.

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.

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.

Compositional temporal grounding (CTG) aims to localize the most relevant segment from an untrimmed video based on a given natural language sentence, and the test samples for this task contain novel components not seen in training. However, existing CTG methods suffer from two shortcomings: (1) Most methods adopt transformers to model global video information only, thus failing to balance the long-range perception and regional representation of video sequences; (2) Due to the lack of aligning videos and sentences at a fine-grained level, the model's capacity for compositional generalization is limited, particularly when query sentences contain novel components. To address these problems, we propose a novel method called Principal Token-aware Adjacent Network (PTAN), which consists of three parts: (1) Principal Temporal Token Recomposition combining video clip-level features obtained from the transformer backbone to capture more significant local features while retaining enough contextual information. (2) Regional Semantic-Aware Learning, which exploits regional representations of videos for cross-modal semantic alignment on the feature space. (3) Principal Semantic-Aware Learning that facilitates fine-grained alignment between visual and textual by sensing principal visual and textual tokens in a self-supervised manner. Extensive experiments on two widely used benchmarks (i.e., Charades-CG and ActivityNet-CG) show that our PTAN method outperforms recent CTG state-of-the-art methods, achieving remarkable improvements in compositional generalization. Our code is available at https://github.com/rushzy/PTAN.

TheComposed Query-Based Image Retrieval (CQBIR)task aims to precisely obtain the preserved and modified parts, based on the multi-grained semantics learned from the composed query. Since the composed query includes a reference image and the modification text, not just a single modality, this task is more challenging than the general image retrieval tasks. Most previous methods attempt to learn preserved and modified parts via different attention modules and fuse them as a unified representation. However, these methods have two intrinsic drawbacks: 1) The different granular semantic information of the composed query is neglected, which results in the fact that learned preserved and modified parts are irrelevant to correct semantics. 2) The preserved and modified parts learned by previous methods have obvious overlaps, which may lead the model to obtain sub-optimal preserved and modified regions. To this end, we propose a novel method termedMulti-Grained Attention Network with Mutual Exclusion (MANME)to address the above problems. Our MANME method mainly consists of two components: 1) A multi-grained semantic construction for obtaining various textual and visual semantic information. 2) An attention with mutual exclusion constraint for reducing the degree of overlap between preserved and modified parts. It adequately utilizes the various granular semantic information and effectively refines the learned preserved and modified parts. Extensive experiments and further analyses on three widely used CQBIR datasets demonstrate that our proposed MANME method achieves new state-of-the-art performance on the CQBIR task.

In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises from the inherent ambiguity in temporal annotations of abnormal occurrences. Inspired by the statistical insight that temporal features of abnormal events often exhibit outlier characteristics, we propose a novel method, BN-WVAD, which incorporates BatchNorm into WVAD. In the proposed BN-WVAD, we leverage the Divergence of Feature from the Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos. The proposed DFM criterion is also discriminative for anomaly recognition and more resilient to label noise, serving as the additional anomaly score to amend the prediction of the anomaly classifier that is susceptible to noisy labels. Moreover, a batch-level selection strategy is devised to filter more abnormal snippets in videos where more abnormal events occur. The proposed BN-WVAD model demonstrates state-of-the-art performance on UCF-Crime with an AUC of 87.24%, and XD-Violence, where AP reaches up to 84.93%. Our code implementation is accessible athttps://github.com/cool-xuan/BN-WVAD.

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.

Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature predominantly focuses on pair-based and proxy-based methods to maximize inter-class discrepancy and minimize intra-class diversity. However, these methods tend to suffer from the collapse of the embedding space due to their over-reliance on label information. This leads to sub-optimal feature representation and inferior model performance. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Specifically, our proposed loss primarily draws inspiration from the principle of Maximal Coding Rate Reduction. It promotes the sparseness of feature clusters in the embedding space to prevent collapse by maximizing the average coding rate of sample features or class proxies. Moreover, we integrate our proposed loss with pair-based and proxy-based methods, resulting in notable performance improvement. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods. Extensive ablation studies verify the effectiveness of our method in preventing embedding space collapse and promoting generalization performance.

Temporal language grounding (TLG) is one of the most challenging cross-modal video understanding tasks, which aims at retrieving the most relevant video segment from an untrimmed video according to a natural language sentence. The existing methods can be separated into two dominant types: 1) proposal-based and 2) proposal-free methods, where the former conduct contextual interactions and the latter localizes timestamps flexibly. However, the constant-scale candidates in proposal-based methods limit the localization precision and bring extra computational costs. In contrast, the proposal-free methods perform well on high-precision metrics-based on the fine-grained features but suffer from a lack of coarse-grained interactions, which cause degeneration when the video becomes complex. In this article, we propose a novel framework termed semantic decoupling network (SDN) that combines the advantages of proposal-based and proposal-free methods and overcomes their defects. It contains three key components: 1) semantic decoupling module (SDM); 2) context modeling block (CMB); and 3) semantic cross-level aggregation module (SCAM). By capturing the video-text contexts in multilevel semantics, the SDM and CMB effectively utilize the benefits of proposal-based methods. Meanwhile, the SCAM maintains the merit of proposal-free methods in that it localizes timestamps precisely. The experiments on three challenge datasets, i.e., Charades-STA, TACoS, and ActivityNet-Caption, show that our proposed SDN method significantly outperforms recent state-of-the-art methods, especially the proposal-free methods. Extensive analyses, as well as the implementation code of the proposed SDN method, are provided at https://github.com/CFM-MSG/Code_SDN.

Embodied question answering (EQA) is a recently emerged research field in which an agent is asked to answer the user's questions by exploring the environment and collecting visual information. Plenty of researchers turn their attention to the EQA field due to its broad potential application areas, such as in-home robots, self-driven mobile, and personal assistants. High-level visual tasks, such as EQA, are susceptible to noisy inputs, because they have complex reasoning processes. Before the profits of the EQA field can be applied to practical applications, good robustness against label noise needs to be equipped. To tackle this problem, we propose a novel label noise-robust learning algorithm for the EQA task. First, a joint training co-regularization noise-robust learning method is proposed for noisy filtering of the visual question answering (VQA) module, which trains two parallel network branches by one loss function. Then, a two-stage hierarchical robust learning algorithm is proposed to filter out noisy navigation labels in both trajectory level and action level. Finally, by taking purified labels as inputs, a joint robust learning mechanism is given to coordinate the work of the whole EQA system. Empirical results demonstrate that, under extremely noisy environments (45% of noisy labels) and low-level noisy environments (20% of noisy labels), the robustness of deep learning models trained by our algorithm is superior to the existing EQA models in noisy environments.

Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitates the incorporation of additional hyperparameters, notably the threshold, which defines whether the sample pair is informative. The threshold provides a stable numerical standard for determining whether to retain the pairs. It is a vital parameter to reduce the redundant sample pairs participating in training. Nonetheless, finding the optimal threshold can be a time-consuming endeavor, often requiring extensive grid searches. Because the threshold cannot be dynamically adjusted in the training stage, we should conduct plenty of repeated experiments to determine the threshold. Therefore, we introduce a novel approach for adjusting the thresholds associated with both the loss function and the sample mining strategy. We design a static Asymmetric Sample Mining Strategy (ASMS) and its dynamic version, the Adaptive Tolerance ASMS (AT-ASMS), tailored for sample mining methods. ASMS utilizes differentiated thresholds to address the problems (too few positive pairs and too many redundant negative pairs) caused by only applying a single threshold to filter samples. The AT-ASMS can adaptively regulate the ratio of positive and negative pairs during training according to the ratio of the currently mined positive and negative pairs. This meta-learning-based threshold generation algorithm utilizes a single-step gradient descent to obtain new thresholds. We combine these two threshold adjustment algorithms to form the Dual Dynamic Threshold Adjustment Strategy (DDTAS). Experimental results show that our algorithm achieves competitive performance on the CUB200, Cars196, and SOP datasets. Our codes are available at https://github.com/NUST-Machine-Intelligence-Laboratory/DDTAS .

In this article, we study the challenging cross-modal image retrieval task,Composed Query-Based Image Retrieval (CQBIR), in which the query is not a single text query but a composed query, i.e., a reference image, and a modification text. Compared with the conventional cross-modal image-text retrieval task, the CQBIR is more challenging as it requires properly preserving and modifying the specific image region according to the multi-level semantic information learned from the multi-modal query. Most recent works focus on extracting preserved and modified information and compositing it into a unified representation. However, we observe that the preserved regions learned by the existing methods contain redundant modified information, inevitably degrading the overall retrieval performance. To this end, we propose a novel method termedCross-ModalAttentionPreservation (CMAP). Specifically, we first leverage the cross-level interaction to fully account for multi-granular semantic information, which aims to supplement the high-level semantics for effective image retrieval. Furthermore, different from conventional contrastive learning, our method introduces self-contrastive learning into learning preserved information, to prevent the model from confusing the attention for the preserved part with the modified part. Extensive experiments on three widely used CQBIR datasets, i.e., FashionIQ, Shoes, and Fashion200k, demonstrate that our proposed CMAP method significantly outperforms the current state-of-the-art methods on all the datasets. The anonymous implementation code of our CMAP method is available at https://github.com/CFM-MSG/Code_CMAP.

Currently, most existing text-based video retrieval methods are only adapted to trimmed videos. However, more complicated untrimmed videos are common in multimedia applications nowadays. In this paper, we focus on the Partially Relevant Video Retrieval (PRVR) task that retrieves untrimmed long videos with partial text descriptions. To tackle this challenging problem, we propose a novel method termed Progressive Event Alignment Network (PEAN) to align text queries with local video content progressively. Specifically, it consists of three key components: (1) A Multimodal Representation Module (MRM) that extracts text representations and hierarchical video representations. (2) An Event Searching Module (ESM) that localizes the described video content roughly. (3) An Event Aligning Module (EAM) that aligns text queries and local video content at a fine-grained level. Additionally, we also design a Gaussian-based pooling strategy in both the ESM and EAM, which thoroughly mines the semantic information in representative video frames. The extensive experiments on three PRVR benchmarks demonstrate our proposed PEAN method significantly outperforms current state-of-the-art methods.

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.

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.

The optical flow guidance strategy is ideal for obtaining motion information of objects in the video. It is widely utilized in video segmentation tasks. However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene. The temporal consistency provided by the optical flow could be effectively supplemented by modeling in a structural form. This paper proposes a new hierarchical graph neural network (GNN) architecture, dubbed hierarchical graph pattern understanding (HGPU), for zero-shot video object segmentation (ZS-VOS). Inspired by the strong ability of GNNs in capturing structural relations, HGPU innovatively leverages motion cues (i.e., optical flow) to enhance the high-order representations from the neighbors of target frames. Specifically, a hierarchical graph pattern encoder with message aggregation is introduced to acquire different levels of motion and appearance features in a sequential manner. Furthermore, a decoder is designed for hierarchically parsing and understanding the transformed multi-modal contexts to achieve more accurate and robust results. HGPU achieves state-of-the-art performance on four publicly available benchmarks (DAVIS-16, YouTube-Objects, Long-Videos and DAVIS-17). Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HGPU.

Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to distinguish between foreground and background or to keep track of the foreground in complex scenarios. The common practice of introducing motion information, such as optical flow, can lead to overreliance on optical flow estimation. To address these challenges, we propose an encoder-decoder-based hierarchical co-attention propagation network (HCPN) capable of tracking and segmenting objects. Specifically, our model is built upon multiple collaborative evolutions of the parallel co-attention module (PCM) and the cross co-attention module (CCM). PCM captures common foreground regions among adjacent appearance and motion features, while CCM further exploits and fuses cross-modal motion features returned by PCM. Our method is progressively trained to achieve hierarchical spatio-temporal feature propagation across the entire video. Experimental results demonstrate that our HCPN outperforms all previous methods on public benchmarks, showcasing its effectiveness for ZS-VOS. Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HCPN.

Weakly supervised semantic segmentation with only image-level labels aims to reduce annotation costs for the segmentation task. Existing approaches generally leverage class activation maps (CAMs) to locate the object regions for pseudo label generation. However, CAMs can only discover the most discriminative parts of objects, thus leading to inferior pixel-level pseudo labels. To address this issue, we propose a saliency guidedInter- andIntra-ClassRelationConstrained (I$^{2}$CRC) framework to assist the expansion of the activated object regions in CAMs. Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes. Further, we propose a class-specific distance module to push the inter-class features apart and encourage the object region to have a higher activation than the background. Besides strengthening the capability of the classification network to activate more integral object regions in CAMs, we also introduce an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels. Extensive experiments on PASCAL VOC 2012 and COCO datasets demonstrate well the effectiveness of I$^{2}$CRC over other state-of-the-art counterparts.

Due to its significant capability of modeling long-range dependencies, vision transformer (ViT) has achieved promising success in both holistic and occluded person re-identification (Re-ID) tasks. However, the inherent problems of transformers such as the huge computational cost and memory footprint are still two unsolved issues that will block the deployment of ViT based person Re-ID models on resource-limited edge devices. Our goal is to reduce both the inference complexity and model size without sacrificing the comparable accuracy on person Re-ID, especially for tasks with occlusion. To this end, we propose a novel attention map guided (AMG) transformer pruning method, which removes both redundant tokens and heads with the guidance of the attention map in a hardware-friendly way. We first calculate the entropy in the key dimension and sum it up for the whole map, and the corresponding head parameters of maps with high entropy will be removed for model size reduction. Then we combine the similarity and first-order gradients of key tokens along the query dimension for token importance estimation and remove redundant key and value tokens to further reduce the inference complexity. Comprehensive experiments on Occluded DukeMTMC and Market-1501 demonstrate the effectiveness of our proposals. For example, our proposed pruning strategy on ViT-Base enjoys29.4%FLOPssavings with0.2%drop on Rank-1 and0.4%improvement on mAP, respectively. Code and models have been made available athttps://github.com/NUST-Machine-Intelligence-Laboratory/AMG.

Webly-supervised fine-grained visual classification (FGVC) has attracted increasing attention in recent years because of the unaffordable cost of obtaining correctly-labeled large-scale fine-grained datasets. However, due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images tends to have an inferior recognition ability. In the literature, to alleviate this issue, loss correction methods try to estimate the noise transition matrix, but the inevitable false correction would cause accumulated errors. Sample selection methods identify clean (“easy”) samples based on the fact that small losses can alleviate the accumulated errors. However, “hard” and mislabeled examples that can both boost the robustness of FG models are also dropped. To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the network. Furthermore, in order to endow our model with the capability to capture richer and more discriminative feature representations, we propose a cross-layer attention-based feature refinement (CLAR) block. We demonstrate the superiority of the proposed approach from both theoretical and experimental perspectives.

The lack of sufficient training data has been one obstacle to fine-grained visual classification research because labeling subcategories generally requires specialist knowledge. As one optional approach to alleviating the data-hunger problem, leveraging web images as training data is drawing increasing attention. Nevertheless, web images potentially have false labels, which can misguide the training process. Although several works have been proposed to deal with label noise, it still can be difficult for the network to tackle complex real-world noisy labels without any prior knowledge. In the literature, we propose to leverage a small and clean meta-set to provide reliable prior knowledge for tackling noisy web images. Specifically, our method trains a network with two peer predicting heads, which learn from noisy web images (web head) and meta ones (meta head), respectively. The meta head produces pseudo soft labels for web images to revise their training loss, which can overcome the high noise ratio problem. Furthermore, a selection net is trained in a meta-learning strategy to identify in- and out-of-distribution noisy images. Then in-distribution ones are reused for training with pseudo soft labels produced by the meta head as supervision, while out-of-distribution ones are discarded. In this manner, the misguidance caused by label noise is remarkably alleviated and in-distribution noisy samples are properly exploited to boost model performance. The superiority of our proposed approach is demonstrated by mathematical theory with great interpretability as well as extensive experimental results on the real-world dataset WebFG-496.

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.

Video events grounding aims at retrieving the most relevant moments from an untrimmed video in terms of a given natural language query. Most previous works focus on Video Sentence Grounding (VSG), which localizes the moment with a sentence query. Recently, researchers extended this task to Video Paragraph Grounding (VPG) by retrieving multiple events with a paragraph. However, we find the existing VPG methods may not perform well on context modeling and highly rely on video-paragraph annotations. To tackle this problem, we propose a novel VPG method termed Semi-supervised Video-Paragraph TRansformer (SVPTR), which can more effectively exploit contextual information in paragraphs and significantly reduce the dependency on annotated data. Our SVPTR method consists of two key components: (1) a base model VPTR that learns the video-paragraph alignment with contrastive encoders and tackles the lack of sentence-level contextual interactions and (2) a semi-supervised learning framework with multimodal feature perturbations that reduces the requirements of annotated training data. We evaluate our model on three widely-used video grounding datasets, i.e., ActivityNet-Caption, Charades-CD-OOD, and TACoS. The experimental results show that our SVPTR method establishes the new state-of-the-art performance on all datasets. Even under the conditions of fewer annotations, it can also achieve competitive results compared with recent VPG methods.

Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data. Prior literature primarily resorts to sample selection methods for combating noisy labels. However, these approaches focus on dividing samples by order sorting or threshold selection, inevitably introducing hyperparameters (e.g., selection ratio / threshold) that are hard-to-tune and dataset-dependent. To this end, we propose a simple yet effective approach named PNP (Probabilistic Noise Prediction) to explicitly model label noise. Specifically, we simultaneously train two networks, in which one predicts the category label and the other predicts the noise type. By predicting label noise probabilistically, we identify noisy samples and adopt dedicated optimization objectives accordingly. Finally, we establish a joint loss for network update by unifying the classification loss, the auxiliary constraint loss, and the in-distribution consistency loss. Comprehensive experimental results on synthetic and realworld datasets demonstrate the superiority of our proposed method. The source code and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/PNP.

Video Paragraph Grounding aims at retrieving multiple relevant moments from an untrimmed video with a given natural language paragraph query. However, the complex paragraph query brings more challenges to the multimodal fusion and context modeling, which limited the performance of existing VPG methods. To this end, we propose a novel framework for VPG in this paper, termed Graph-based Transformer with Language Reconstruction (GTLR). It consists of three components: (1) Multimodal Graph Encoder conducting the graph reasoning for video-text fusion. (2) Event-wise Decoder predicting the timestamps based on multiple sentence-level features. (3) Language Reconstructor rebuilding the paragraph queries and making our model explainable. We adopt two benchmarks, i.e., ActivityNet-Caption and Charades-STA, to evaluate our model and conduct comprehensive experiments to analyze the effectiveness of each component. The experimental results show that our GTLR method outperforms recent state-of-the-art 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.

In skeleton-based action recognition, Graph Convolutional Networks (GCNs) have achieved remarkable performance since the skeleton representation of human action can be naturally modeled by the graph structure. Most of the existing GCN-based methods extract skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale contextual information. Besides, the commonly used strided convolution in temporal dimension could evenly filters out the keyframes we expect to preserve and leads to the loss of keyframe information. To address these issues, we propose a novel Selective Hypergraph Convolution Network, dubbed Selective-HCN, which stacks two key modules: Selective-scale Hypergraph Convolution (SHC) and Selective-frame Temporal Convolution (STC). The SHC module represents the human skeleton as the graph and hypergraph to fully extract multi-scale information, and selectively fuse features at various scales. Instead of traditional strided temporal convolution, the STC module can adaptively select keyframes and filter redundant frames according to the importance of the frames. Extensive experiments on two challenging skeleton action benchmarks, i.e., NTU-RGB+D and Skeleton-Kinetics, demonstrate the superiority and effectiveness of our proposed method.

The Weakly-Supervised Audio-Visual Video Parsing (AVVP) task aims to parse a video into temporal segments and predict their event categories in terms of modalities, labeling them as either audible, visible, or both. Since the temporal boundaries and modalities annotations are not provided, only video-level event labels are available, this task is more challenging than conventional video understanding tasks.Most previous works attempt to analyze videos by jointly modeling the audio and video data and then learning information from the segment-level features with fixed lengths. However, such a design exist two defects: 1) The various semantic information hidden in temporal lengths is neglected, which may lead the models to learn incorrect information; 2) Due to the joint context modeling, the unique features of different modalities are not fully explored. In this paper, we propose a novel AVVP framework termedDual Hierarchical Hybrid Network (DHHN) to tackle the above two problems. Our DHHN method consists of three components: 1) A hierarchical context modeling network for extracting different semantics in multiple temporal lengths; 2) A modality-wise guiding network for learning unique information from different modalities; 3) A dual-stream framework generating audio and visual predictions separately. It maintains the best adaptions on different modalities, further boosting the video parsing performance. Extensive quantitative and qualitative experiments demonstrate that our proposed method establishes the new state-of-the-art performance on the AVVP task.

Zero-Shot Cross-Modal Retrieval (ZS-CMR) has recently drawn increasing attention as it focuses on a practical retrieval scenario, i.e., the multimodal test set consists of unseen classes that are disjoint with seen classes in the training set. The recently proposed methods typically adopt the generative model as the main framework to learn a joint latent embedding space to alleviate the modality gap. Generally, these methods largely rely on auxiliary semantic embeddings for knowledge transfer across classes and unconsciously neglect the effect of the data reconstruction manner in the adopted generative model. To address this issue, we propose a novel ZS-CMR model termed Multimodal Disentanglement Variational AutoEncoders (MDVAE), which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE). Specifically, DVAE is developed to disentangle the original representations of each modality into modality-invariant and modality-specific features. FVAE is designed to fuse and exchange information of multimodal data by the reconstruction and alignment process without pre-extracted semantic embeddings. Moreover, an advanced counter-intuitive cross-reconstruction scheme is further proposed to enhance the informativeness and generalizability of the modality-invariant features for more effective knowledge transfer. The comprehensive experiments on four image-text retrieval and two image-sketch retrieval datasets consistently demonstrate that our method establishes the new state-of-the-art performance.

Binary optimization problems (BOPs) arise naturally in many fields, such as information retrieval, computer vision, and machine learning. Most existing binary optimization methods either use continuous relaxation which can cause large quantization errors, or incorporate a highly specific algorithm that can only be used for particular loss functions. To overcome these difficulties, we propose a novel generalized optimization method, named Alternating Binary Matrix Optimization (ABMO), for solving BOPs. ABMO can handle BOPs with/without orthogonality or linear constraints for a large class of loss functions. ABMO involves rewriting the binary, orthogonality and linear constraints for BOPs as an intersection of two closed sets, then iteratively dividing the original problems into several small optimization problems that can be solved as closed forms. To provide a strict theoretical convergence analysis, we add a sufficiently small perturbation and translate the original problem to an approximated problem whose feasible set is continuous. We not only provide rigorous mathematical proof for the convergence to a stationary and feasible point, but also derive the convergence rate of the proposed algorithm. The promising results obtained from four binary optimization tasks validate the superiority and the generality of ABMO compared with the state-of-the-art methods.

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.

Recently, deep learning techniques have achieved significant improvements in unsupervised video object segmentation (UVOS). However, many of existing approach cannot accurately identify the foreground objects and the background as they commonly use the coarse temporal features (e.g., optical flow and multi-frames attention). In this paper, we present a novel model termed Flow Edge-based Motion-Attentive Network (FEM-Net), to address the unsupervised video object segmentation problem. Firstly, a motion-attentive encoder is used to jointly learn the spatial and temporal features. Then, a Flow Edge Connect (FEC) module is designed to hallucinate edges of the ambiguous or missing region in the optical flow. During the segmentation stage, the complementary temporal feature composed by the motion-attentive feature and flow edge is fed into a decoder to infer the salient foreground objects. Experimental results on two challenging public benchmarks (i.e. DAVIS-16 and FBMS) demonstrate that the proposed FEM-Net compares favorably against the state-of-the-art methods.

Autonomous driving relies on trusty visual recognition of surrounding objects. Few-shot image classification is used in autonomous driving to help recognize objects that are rarely seen. Successful embedding and metric-learning approaches to this task normally learn a feature comparison framework between an unseen image and the labeled images. However, these approaches usually have problems with ambiguous feature embedding because they tend to ignore important local visual and semantic information when extracting intra-class common features from the images. In this paper, we introduce a Semantic-Aligned Attention (SAA) mechanism to refine feature embedding and it can be applied to most of the existing embedding and metric-learning approaches. The mechanism highlights pivotal local visual information with attention mechanism and aligns the attentive map with semantic information to refine the extracted features. Incorporating the proposed mechanism into the prototypical network, evaluation results reveal competitive improvements in both few-shot and zero-shot classification tasks on various benchmark datasets.

One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. However, these existing approaches simulate the test conditions too strictly during the training process, and thus cannot make full use of the given label information. Besides, these approaches mainly focus on the foreground-background target class segmentation setting. They only utilize binary mask labels for training. In this paper, we propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category. After integrating the target class cues into the query features, we then propose a pyramid feature fusion module to mine the fused features for the final classifier. Furthermore, to take more advantage of the support image-mask pair, we propose a self-prototype guidance branch to support image segmentation. It can constrain the network for generating more compact features and a robust prototype for each semantic class. For inference, we propose a fused prototype guidance branch for the segmentation of the query image. Specifically, we leverage the prediction of the query image to extract the pseudo-prototype and combine it with the initial prototype. Then we utilize the fused prototype to guide the final segmentation of the query image. Extensive experiments demonstrate the superiority of our proposed approach. The source codes and models have been made available athttps://github.com/NUST-Machine-Intelligence-Laboratory/SMCP.

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. Therefore, in this paper, we propose a novel approach for removing irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at:https://github.com/NUST-Machine-Intelligence-Laboratory/Advanced-Softly-Update-Drop.

Video moment retrieval with text query aims to retrieve the most relevant segment from the whole video based on the given text query. It is a challenging cross-modal alignment task due to the huge gap between visual and linguistic modalities and the noise generated by manual labeling of time segments. Most of the existing works only use language information in the cross-modal fusion stage, neglecting that language information plays an important role in the retrieval stage. Besides, these works roughly compress the visual information in the video clips to reduce the computation cost which loses subtle video information in the long video. In this paper, we propose a novel model termed Cross-modal Dynamic Networks (CDN) which dynamically generates convolution kernel by visual and language features. In the feature extraction stage, we also propose a frame selection module to capture the subtle video information in the video segment. By this approach, the CDN can reduce the impact of the visual noise without significantly increasing the computation cost and leads to a better video moment retrieval result. The experiments on two challenge datasets,i.e., Charades-STA and TACoS, show that our proposed CDN method outperforms a bundle of state-of-the-art methods with more accurately retrieved moment video clips. The implementation code and extensive instruction of our proposed CDN method are provided athttps://github.com/CFM-MSG/Code_CDN.

Due to its powerful capability of representation learning and efficient computation, deep hashing has made significant progress in large-scale image retrieval. It has been recognized that deep neural networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in deep hashing-based retrieval field. In this paper, we propose a novel prototype-supervised adversarial network (ProS-GAN), which formulates a flexible generative architecture for efficient and effective targeted hashing attack.To the best of our knowledge, this is one of the first generation-based methods to attack deep hashing networks. Generally, our proposed framework consists of three parts,i.e., a PrototypeNet, a Generator and a Discriminator. Specifically, the designed PrototypeNet embeds the target label into the semantic representation and learns the prototype code as the category-level representative of the target label. Moreover, the semantic representation and the original image are jointly fed into the generator for flexible targeted attack. Particularly, the prototype code is adopted to supervise the generator to construct the targeted adversarial example by minimizing the Hamming distance between the hash code of the adversarial example and the prototype code. Furthermore, the generator fools the discriminator to simultaneously encourage the adversarial examples visually realistic and the semantic representation informative. Extensive experiments demonstrate that the proposed framework can efficiently produce adversarial examples with better targeted attack performance and transferability over state-of-the-art targeted attack methods of deep hashing. The source code is available athttps://github.com/xunguangwang/ProS-GAN_Trans.

Manually labeling fine-grained datasetsis laborious and typically requires domain-specific expert knowledge. Conversely, a vast amount of web data is relatively easy to obtain with nearly no human effort. Therefore, learning from noisy web data for fine-grained tasks is attracting increasing attention in recent years. However, the presence of noise in web images is a huge obstacle for training robust fine-grained recognition models. To this end, we propose a novel approach to identify noisy images as well as specifically distinguish in- and out-of-distribution samples. It can purify the noisy web training set by discarding out-of-distribution noise and relabeling in-distribution noisy samples. Then we can train the model on the purified dataset to alleviate the harmful effects of noise and make the most of web images to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at:https://github.com/NUST-Machine-Intelligence-Laboratory/Dataset-Purification.

MixUp is an effective data augmentation method to regularize deep neural networks via random linear interpolations between pairs of samples and their labels. It plays an important role in model regularization, semisupervised learning (SSL), and domain adaption. However, despite its empirical success, its deficiency of randomly mixing samples has poorly been studied. Since deep networks are capable of memorizing the entire data set, the corrupted samples generated by vanilla MixUp with a badly chosen interpolation policy will degrade the performance of networks. To overcome overfitting to corrupted samples, inspired by metalearning (learning to learn), we propose a novel technique of learning to a mixup in this work, namely, MetaMixUp. Unlike the vanilla MixUp that samples interpolation policy from a predefined distribution, this article introduces a metalearning-based online optimization approach to dynamically learn the interpolation policy in a data-adaptive way (learning to learn better). The validation set performance via metalearning captures the noisy degree, which provides optimal directions for interpolation policy learning. Furthermore, we adapt our method for pseudolabel-based SSL along with a refined pseudolabeling strategy. In our experiments, our method achieves better performance than vanilla MixUp and its variants under SL configuration. In particular, extensive experiments show that our MetaMixUp adapted SSL greatly outperforms MixUp and many state-of-the-art methods on CIFAR-10 and SVHN benchmarks under the SSL configuration.

The recent success of audio-visual representations learning can be largely attributed to their pervasive concurrency property, which can be used as a self-supervision signal and extract correlation information. While most recent works focus on capturing the shared associations between the audio and visual modalities, they rarely consider multiple audio and video pairs at once and pay little attention to exploiting the valuable information within each modality. To tackle this problem, we propose a novel audio-visual representation learning method dubbed self-supervised curriculum learning (SSCL) under the teacher-student learning manner. Specifically, taking advantage of contrastive learning, a two-stage scheme is exploited, which transfers the cross-modal information between teacher and student model as a phased process. The proposed SSCL approach regards the pervasive property of audiovisual concurrency as latent supervision and mutually distills the structure knowledge of visual to audio data. Notably, the SSCL method can learn discriminative audio and visual representations for various downstream applications. Extensive experiments conducted on both action video recognition and audio sound recognition tasks show the remarkably improved performance of the SSCL method compared with the state-of-the-art self-supervised audio-visual representation learning methods.

Due to its powerful capability of representation learning and high-efficiency computation, deep hashing has made significant progress in large-scale image retrieval. However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field. In this paper, we propose a novel prototype-supervised adversarial network (ProS-GAN), which formulates a flexible generative architecture for efficient and effective targeted hashing attack. To the best of our knowledge, this is the first generation-based method to attack deep hashing networks. Generally, our proposed framework consists of three parts, i.e., a PrototypeNet, a generator and a discriminator. Specifically, the designed PrototypeNet embeds the target label into the semantic representation and learns the prototype code as the category-level representative of the target label. Moreover, the semantic representation and the original image are jointly fed into the generator for flexible targeted attack. Particularly, the prototype code is adopted to supervise the generator to construct the targeted adversarial example by minimizing the Hamming distance between the hash code of the adversarial example and the prototype code. Furthermore, the generator is against the discriminator to simultaneously encourage the adversarial examples visually realistic and the semantic representation informative. Extensive experiments verify that the proposed framework can efficiently produce adversarial examples with better targeted attack performance and transferability over state-of-the-art targeted attack methods of deep hashing.

Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image’s salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network’s ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network’s self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods. The source codes are available at https://github.com/NUST-Machine-Intelligence-Laboratory/nsrom.

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily adopt a sample selection strategy, which selects small-loss samples for subsequent training. However, prior literature tends to perform sample selection within each mini-batch, neglecting the imbalance of noise ratios in different mini-batches. Moreover, valuable knowledge within high-loss samples is wasted. To this end, we propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency). Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution. Furthermore, we propose a joint loss to advance the model generalization performance by introducing consistency regularization. Extensive experiments have validated the superiority of our approach over existing state-of-the-art methods. The source code and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/Jo-SRC.

Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distinguishing subordinate categories, it will significantly reduce the labeling costs by leveraging free web data. Despite its significant practical and research value, the webly supervised fine-grained recognition problem is not extensively studied in the computer vision community, largely due to the lack of high-quality datasets. To fill this gap, in this paper we construct two new benchmark webly supervised fine-grained datasets, termed WebFG-496 and WebiNat-5089, respectively. In concretely, WebFG-496 consists of three sub-datasets containing a total of 53,339 web training images with 200 species of birds (Web-bird), 100 types of aircrafts (Web-aircraft), and 196 models of cars (Web-car). For WebiNat-5089, it contains 5089 sub-categories and more than 1.1 million web training images, which is the largest webly supervised fine-grained dataset ever. As a minor contribution, we also propose a novel webly supervised method (termed "Peer-learning") for benchmarking these datasets. Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art. Our benchmark datasets and the source codes of Peer-learning have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset.

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.

Graph convolution networks (GCNs) have become the mainstream framework for the skeleton-based action recognition task, since the skeleton representation of human action can be naturally modeled by the graph structure. Generally, most of the existing GCN based models extract and aggregate skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale information such as part and body features in the skeleton. To address this issue, we propose a novel Graph Convolutional Hourglass Network (GCHN) model, which is scalable by stacking several basic modules of Graph Convolutional Hourglass Block (GCHB). Each GCHB module consists of the sequential operations of graph convolution, graph pooling and graph unpooling, which can promote the interaction of multi-scale information in the skeleton and effectively improve the recognition performance. Extensive experiments on the challenging NTU-RGB+D and Kinetics-Skeleton datasets demonstrate that the proposed GCHN model achieves state-of-the-art performance.

Graph neural networks (GNNs) have been widely used in the 3D human pose estimation task, since the pose representation of a human body can be naturally modeled by the graph structure. Generally, most of the existing GNN-based models utilize the restricted receptive fields of filters and single-scale information, while neglecting the valuable multi-scale contextual information. To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. In our proposed PoseGTAC model, Graph Atrous Convolution (GAC) and Graph Transformer Layer (GTL), respectively for the extraction of local multi-scale and global long-range information, are combined and stacked in an encoder-decoder structure, where graph pooling and unpooling are adopted for the interaction of multi-scale information from local to global (e.g., part-scale and body-scale). Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that the proposed PoseGTAC model exceeds all previous methods and achieves state-of-the-art performance.

In recent years, the Internet has stimulated the explosion of multimedia data. Food-related cooking videos, images, and recipes promote the rapid development of food computing. Image-recipe retrieval is an important sub-task in the field of cross-modal retrieval, which focuses on the measurement of the association between food image and recipe (title, ingredients, instructions). Although the existing methods have proposed some feasible solutions to achieve the goal of Image-recipe retrieval, there are still the following issues: 1) complex model structure and time-consuming training process. 2) the lack of information interaction within modalities and information integration between images and recipes. To this end, we propose a novel lightweight framework namedIntra- and Inter-Modality Hybrid Fusion (IMHF). Our IMHF model abandons a separate deep vision encoder and utilizes the transformer module to unify the visual and text features. In this way, valuable information from images and recipes can be condensed and the direct information interaction between the two modalities can be promoted. Both the intra- and inter-modality fusion can be realized. Extensive experiment results on the large-scale benchmark dataset Recipe1M demonstrate that our model IMHF with a lightweight architecture is superior to the state-of-the-art approaches.

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.

Without appealing to exhaustive labeled data, self-supervised monocular depth estimation (MDE) plays a fundamental role in computer vision. Previous methods usually adopt a one-stage MDE network, which is insufficient to achieve high performance. In this paper, we dig deep into this task to propose an aggressive framework termed AggNet. The framework is based on a training-only progressive two-stage module to perform pseudo counter-surveillance as well as a simple yet effective dual-warp loss function between image pairs. In particular, we first propose a residual module, which follows the MDE network to learn a refined depth. The residual module takes both the initial depth generated from MDE and the initial color image as input to generate refined depth with residual depth learning. Then, the refined depth is leveraged to supervise the initial depth simultaneously during the training period. For inference, only the MDE network is retained to regress depth from a single image, which gains better performance without introducing extra computation. In addition to self-distillation loss, a simple yet effective dual-warp consistency loss is introduced to encourage the MDE network to keep depth consistency between stereo image pairs. Extensive experiments show that our AggNet achieves state-of-the-art performance on the KITTI and Make3D datasets.

Joint learning of scene parsing and depth estimation remains a challenging task due to the rivalry between the two tasks. In this paper, we revisit the mutual enhancement for joint semantic segmentation and depth estimation. Inspired by the observation that the competition and cooperation could be reflected in the feature frequency components of different tasks, we propose a Frequency Aware Feature Enhancement (FAFE) network that can effectively enhance the reciprocal relationship whereas avoiding the competition. In FAFE, a frequency disentanglement module is proposed to fetch the favorable frequency component sets for each task and resolve the discordance between the two tasks. For task cooperation, we introduce a re-calibration unit to aggregate features of the two tasks, so as to complement task information with each other. Accordingly, the learning of each task can be boosted by the complementary task appropriately. Besides, a novel local-aware consistency loss function is proposed to impose on the predicted segmentation and depth so as to strengthen the cooperation. With the FAFE network and new local-aware consistency loss encapsulated into the multi-task learning network, the proposed approach achieves superior performance over previous state-of-the-art methods. Extensive experiments and ablation studies on multi-task datasets demonstrate the effectiveness of our proposed approach.

Temporal action detection aims to locate specific segments of action instances in an untrimmed video. Most existing approaches commonly extract the features of all candidate video segments and then classify them separately. However, they may neglect the underlying relationship among candidates unconsciously. In this paper, we propose a novel model termed Candidate-Aware Aggregation (CAA) to tackle this problem. In CAA, we design the Global Awareness (GA) module to exploit long-range relations among all candidates from a global perspective, which enhances the features of action instances. The GA module is then embedded into a multi-level hierarchical network named FENet, to aggregate local features in adjacent candidates to suppress background noise. As a result, the relationship among candidates is explicitly captured from both local and global perspectives, which ensures more accurate prediction results for the candidates. Extensive experiments conducted on two popular benchmarks ActivityNet-1.3 and THUMOS-14 demonstrate the superiority of CAA comparing to the state-of-the-art methods.

Zero-shot sketch-based image retrieval is challenging for the modal gap between distributions of sketches and images and the inconsistency of label spaces during training and testing. Previous methods mitigate the modal gap by projecting sketches and images into a joint embedding space. Most of them also bridge seen and unseen classes by leveraging semantic embeddings, i.e., word vectors and hierarchical similarities. In this paper, we propose Relationship-Preserving Knowledge Distillation (RPKD) to study generalizable embeddings from the perspective of knowledge distillation bypassing the usage of semantic embeddings. In particular, we firstly distill the instance-level knowledge to preserve inter-class relationships without semantic similarities that require extra effort to collect. We also reconcile the contrastive relationships among instances between different embedding spaces, which is complementary to instance-level relationships. Furthermore, embedding-induced supervision, which measures the similarities of an instance to partial class embedding centers from the teacher, is developed to align the student's classification confidences. Extensive experiments conducted on three benchmark ZS-SBIR datasets, i.e., Sketchy, TU-Berlin, and QuickDraw, demonstrate the superiority of our proposed RPKD approach comparing to the state-of-the-art methods.

Contrastive-based self-supervised learning for image representations has significantly closed the gap with supervised learning. A natural extension of image-based contrastive learning methods to the video domain is to fully exploit the temporal structure presented in videos. We propose a novel contrastive self-supervised video representation learning framework, termed Graph Contrastive Augmentation (GCA), by constructing a video temporal graph and devising a graph augmentation that is designed to enhance the correlation across frames of videos and developing a new view for exploring temporal structure in videos. Specifically, we construct the temporal graph in the video by leveraging the relational knowledge behind the correlated sequence video features. Afterwards, we apply the proposed graph augmentation to generate another graph view by cooperating random corruption of the original graph to enhance the diversity of the intrinsic structure of the temporal graph. To this end, we provide two different kinds of contrastive learning methods to train our framework using temporal relationships concealed in videos as self-supervised signals. We perform empirical experiments on downstream tasks, action recognition and video retrieval, using the learned video representation, and the results demonstrate that with the graph view of temporal structure, our proposed GCA remarkably improves performance against or on par with the recent methods.

Optical degradation blurs text shapes and edges, so existing scene text recognition methods have difficulties in achieving desirable results on low-resolution (LR) scene text images acquired in real-world environments. The above problem can be solved by efficiently extracting sequential information to reconstruct super-resolution (SR) text images, which remains a challenging task. In this paper, we propose a Parallelly Contextual Attention Network (PCAN), which effectively learns sequence-dependent features and focuses more on high-frequency information of the reconstruction in text images. Firstly, we explore the importance of sequence-dependent features in horizontal and vertical directions parallelly for text SR, and then design a parallelly contextual attention block to adaptively select the key information in the text sequence that contributes to image super-resolution. Secondly, we propose a hierarchically orthogonal texture-aware attention module and an edge guidance loss function, which can help to reconstruct high-frequency information in text images. Finally, we conduct extensive experiments on TextZoom dataset, and the results can be easily incorporated into mainstream text recognition algorithms to further improve their performance in LR image recognition. Besides, our approach exhibits great robustness in defending against adversarial attacks on seven mainstream scene text recognition datasets, which means it can also improve the security of the text recognition pipeline. Compared with directly recognizing LR images, our method can respectively improve the recognition accuracy of ASTER, MORAN, and CRNN by 14.9%, 14.0%, and 20.1%. Our method outperforms eleven state-of-the-art (SOTA) SR methods in terms of boosting text recognition performance. Most importantly, it outperforms the current optimal text-orient SR method TSRN by 3.2%, 3.7%, and 6.0% on the recognition accuracy of ASTER, MORAN, and CRNN respectively.

Image-recipe retrieval, which aims at retrieving the relevant recipe from a food image and vice versa, is now attracting widespread attention, since sharing food-related images and recipes on the Internet has become a popular trend. Existing methods have formulated this problem as a typical cross-modal retrieval task by learning the image-recipe similarity. Though these methods have made inspiring achievements for image-recipe retrieval, they may still be less effective to jointly incorporate the three crucial points: (1) the association between ingredients and instructions, (2) fine-grained image information, and (3) the latent alignment between recipes and images. To this end, we propose a novel framework namedHybrid Fusion with Intra- and Cross-Modality Attention (HF-ICMA) to learn accurate image-recipe similarity. Our HF-ICMA model adopts an intra-recipe fusion module to focus on the interaction between ingredients and instructions within a recipe, and further enriches the expressions of the two separate embeddings. Meanwhile, an image-recipe fusion module is devised to explore the potential relationship between fine-grained image regions and ingredients from the recipe, which jointly forms the final image-recipe similarity from both the local and global aspects. Extensive experiments on the large-scale benchmark dataset Recipe1M show that our model significantly outperforms the state-of-the-art approaches on various image-recipe retrieval scenarios.

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.

Person Re-identification (ReID) aims to retrieve the pedestrian with the same identity across different views. Existing studies mainly focus on improving accuracy, while ignoring their efficiency. Recently, several hash based methods have been proposed. Despite their improvement in efficiency, there still exists an unacceptable gap in accuracy between these methods and real-valued ones. Besides, few attempts have been made to simultaneously explicitly reduce redundancy and improve discrimination of hash codes, especially for short ones. Integrating Mutual learning may be a possible solution to reach this goal. However, it fails to utilize the complementary effect of teacher and student models. Additionally, it will degrade the performance of teacher models by treating two models equally. To address these issues, we propose a salience-guided iterative asymmetric mutual hashing (SIAMH) to achieve high-quality hash code generation and fast feature extraction. Specifically, a salience-guided self-distillation branch (SSB) is proposed to enable SIAMH to generate hash codes based on salience regions, thus explicitly reducing the redundancy between codes. Moreover, a novel iterative asymmetric mutual training strategy (IAMT) is proposed to alleviate drawbacks of common mutual learning, which can continuously refine the discriminative regions for SSB and extract regularized dark knowledge for two models as well. Extensive experiment results on five widely used datasets demonstrate the superiority of the proposed method in efficiency and accuracy when compared with existing state-of-the-art hashing and real-valued approaches. The code is released at https://github.com/Vill-Lab/SIAMH.

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.

Semantic-preserving hashing establishes efficient multimedia retrieval by transferring knowledge from original data to hash codes so that the latter can preserve the underlying visual and semantic similarities. However, it becomes a crucial bottleneck: how to effectively bridge the trilateral domain gaps (i.e., the visual, semantic, and hashing spaces) to further improve the retrieval accuracy. In this article, we propose an inductive structure consistent hashing (ISCH) method, which can interactively coordinate the semantic correlations between the visual feature space, the binary class space, and the discrete hashing space. Specifically, an inductive semantic space is formulated by a simple multilayer stacking class-encoder, which transforms the naive class information into flexible semantic embeddings. Meanwhile, we design a semantic dictionary learning model to facilitate the bilateral visual-semantic bridging and guide the class-encoder toward reliable semantics, which could well alleviate the visual-semantic bias problem. In particular, the visual descriptors and respective semantic class representations are regularized with a coinciding alignment module. In order to generate privileged hash codes, we further explore semantic and prototype binary code learning to jointly quantify the semantic and latent visual representations into unified discrete hash codes. Moreover, an efficient optimization algorithm is developed to address the resulting discrete programming problem. Comprehensive experiments conducted on four large-scale data sets, i.e., CIFAR-10, NUSWIDE, ImageNet, and MSCOCO, demonstrate the superiority of our method over the state-of-the-art alternatives against different evaluation protocols.

Labeling objects at the subordinate level typically requires expert knowledge, which is not always available from a random annotator. Accordingly, learning directly from web images for fine-grained visual classification (FGVC) has attracted broad attention. However, the existence of noise in web images is a huge obstacle for training robust deep neural networks. In this paper, we propose a novel approach to remove irrelevant samples from the real-world web images during training, and only utilize useful images for updating the networks. Thus, our network can alleviate the harmful effects caused by irrelevant noisy web images to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to state-of-the-art webly supervised methods. The data and source code of this work have been made anonymously available at: https://github.com/z337-408/WSNFGVC.

Conventional unsupervised hashing methods usually take advantage of similarity graphs, which are either pre-computed in the high-dimensional space or obtained from random anchor points. On the one hand, existing methods uncouple the procedures of hash function learning and graph construction. On the other hand, graphs empirically built upon original data could introduce biased prior knowledge of data relevance, leading to sub-optimal retrieval performance. In this paper, we tackle the above problems by proposing an efficient and adaptive code-driven graph, which is updated by decoding in the context of an auto-encoder. Specifically, we introduce into our framework twin bottlenecks (i.e., latent variables) that exchange crucial information collaboratively. One bottleneck (i.e., binary codes) conveys the high-level intrinsic data structure captured by the code-driven graph to the other (i.e., continuous variables for low-level detail information), which in turn propagates the updated network feedback for the encoder to learn more discriminative binary codes. The auto-encoding learning objective literally rewards the code-driven graph to learn an optimal encoder. Moreover, the proposed model can be simply optimized by gradient descent without violating the binary constraints. Experiments on benchmarked datasets clearly show the superiority of our framework over the state-of-the-art hashing methods. Our source code can be found at https://github.com/ymcidence/TBH.

The research on scene text recognition (STR) has made remarkable progress in recent years with the development of deep neural networks (DNNs). Recent studies on adversarial attack have verified that a DNN model designed for non-sequential tasks (e.g., classification, segmentation and retrieval) can be easily fooled by adversarial examples. Actually, STR is an application highly related to security issues. However, there are few studies considering the safety and reliability of STR models that make sequential prediction. In this paper, we make the first attempt in attacking the state-of-the-art DNN-based STR models. Specifically, we propose a novel and efficient optimization-based method that can be naturally integrated to different sequential prediction schemes, i.e., connectionist temporal classification (CTC) and attention mechanism. We apply our proposed method to five state-of-the-art STR models with both targeted and untargeted attack modes, the comprehensive results on 7 real-world datasets and 2 synthetic datasets consistently show the vulnerability of these STR models with a significant performance drop. Finally, we also test our attack method on a real-world STR engine of Baidu OCR, which demonstrates the practical potentials of our method.

Sketch-Based Image Retrieval (SBIR) is an emerging research area with many real-world applications. Recent studies have approached this research task under the more challenging zero-shot learning setting (ZS-SBIR), which assume classes in the target domain are unseen during the training stage. Many of the existing ZS-SBIR studies transferred the learned cross-modal (i.e., sketch and image) representations from the source domain to the target domain by leveraging side information in semantic embeddings. However, these ZS-SBIR methods are not able to generalize well to a more realistic setting to retrieve images from seen and unseen classes. To address the limitation of existing methods, we propose the cOmmon Conditional Encoder Adversarial Network (OCEAN) to perform generalized zero-shot sketch-based image retrieval (GZS-SBIR). The OCEAN model utilizes a dual learning framework to cyclically map the sketch and image features to a common semantic space, and project semantic features back to the relevant visual space by adversarial training. We conduct experiments on two publicly available datasets and demonstrate that our proposed model outperformed the state-of-the-arts baselines in both ZS-SBIR and GZS-SBIR tasks.

Recently, developing temporally consistent video-based processing techniques has drawn increasing attention due to the defective extend-ability of existing image-based processing algorithms (e.g., filtering, enhancement, colorization, etc). Generally, applying these image-based algorithms independently to each video frame typically leads to temporal flickering due to the global instability of these algorithms. In this paper, we consider enforcing temporal consistency in a video as a temporal denoising problem that removing the flickering effect in given unstable pre-processed frames. Specifically, we propose a novel model termed Temporal Denoising Mask Synthesis Network (TDMS-Net) that jointly predicts the motion mask, soft optical flow and the refining mask to synthesize the temporal consistent frames. The temporal consistency is learned from the original video and the learned temporal features are applied to reprocess the output frames that are agnostic (blind) to specific image-based processing algorithms. Experimental results on two datasets for 16 different applications demonstrate that the proposed TDMS-Net significantly outperforms two state-of-the-art blind temporal consistency approaches.

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.

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.

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.

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.

Despite great success has been achieved in activity analysis, it still has many challenges. Most existing works in activity recognition pay more attention to designing efficient architecture or video sampling strategy. However, due to the property of fine-grained action and long term structure in video, activity recognition is expected to reason temporal relation between video sequences. In this paper, we propose an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales. Specifically, we construct learnable temporal relation graphs to explore temporal relation on the multi-scale range. Additionally, to facilitate multi-scale temporal relation extraction, we design a multi-head temporal adjacent matrix to represent multi-kinds of temporal relations. Eventually, a multi-head temporal relation aggregator is proposed to extract the semantic meaning of those features convolving through the graphs. Extensive experiments are performed on widely-used large-scale datasets, such as Something-Something, Charades and Jester, and the results show that our model can achieve stateof- the-art performance. Further analysis shows that temporal relation reasoning with our TRG can extract discriminative features for activity recognition.

The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models developing. However, constructing labeled image datasets is laborious and monotonous. To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries. We aim at collecting diverse and accurate images for given queries from the Web. Specifically, we formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately. Our proposed approach not only improves the accuracy but also enhances the diversity of the selected images. To verify the effectiveness of our proposed approach, we construct an image dataset with 100 categories. The experiments show significant performance gains by using the generated data of our approach on several tasks, such as image classification, cross-dataset generalization, and object detection. The proposed method also consistently outperforms existing weakly supervised and web-supervised approaches.

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.

Studies present that dividing categories into subcategories contributes to better image classification. Existing image subcategorization works relying on expert knowledge and labeled images are both time-consuming and labor-intensive. In this article, we propose to select and subsequently classify images into categories and subcategories. Specifically, we first obtain a list of candidate subcategory labels from untagged corpora. Then, we purify these subcategory labels through calculating the relevance to the target category. To suppress the search error and noisy subcategory label-induced outlier images, we formulate outlier images removing and the optimal classification models learning as a unified problem to jointly learn multiple classifiers, where the classifier for a category is obtained by combining multiple subcategory classifiers. Compared with the existing subcategorization works, our approach eliminates the dependence on expert knowledge and labeled images. Extensive experiments on image categorization and subcategorization demonstrate the superiority of our proposed approach.

In this work, we study the robustness of a CNN+RNN based image captioning system being subjected to adversarial noises. We propose to fool an image captioning system to generate some targeted partial captions for an image polluted by adversarial noises, even the targeted captions are totally irrelevant to the image content. A partial caption indicates that the words at some locations in this caption are observed, while words at other locations are not restricted. It is the first work to study exact adversarial attacks of targeted partial captions. Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables. Both the generalized expectation maximization algorithm and structural SVMs with latent variables are then adopted to optimize the problem. The proposed methods generate very successful attacks to three popular CNN+RNN based image captioning models. Furthermore, the proposed attack methods are used to understand the inner mechanism of image captioning systems, providing the guidance to further improve automatic image captioning systems towards human captioning.

Sketch-based 3D shape retrieval has been extensively studied in recent works, most of which focus on improving the retrieval accuracy, whilst neglecting the efficiency. In this paper, we propose a novel framework for efficient sketch-based 3D shape retrieval, i.e., Deep Sketch-Shape Hashing (DSSH), which tackles the challenging problem from two perspectives. Firstly, we propose an intuitive 3D shape representation method to deal with unaligned shapes with arbitrary poses. Specifically, the proposed Segmented Stochastic-viewing Shape Network models discriminative 3D representations by a set of 2D images rendered from multiple views, which are stochastically selected from non-overlapping spatial segments of a 3D sphere. Secondly, Batch-Hard Binary Coding (BHBC) is developed to learn semantics-preserving compact binary codes by mining the hardest samples. The overall framework is jointly learned by developing an alternating iteration algorithm. Extensive experimental results on three benchmarks show that DSSH improves both the retrieval efficiency and accuracy remarkably, compared to the state-of-the-art methods.

Semantic segmentation is an important computer vision task, which aims to allocate a semantic label to each pixel in an image. When training a segmentation model, it is common to fine-tune a classification network pre-trained on a large-scale dataset. However, as an intrinsic property of the classification model, invariance to spatial perturbation resulting from the lose of detail-sensitivity prevents segmentation networks from achieving high performance. The use of standard poolings is one of the key factors for this invariance. The most common standard poolings are max and average pooling. Max pooling can increase both the invariance to spatial perturbations and the non-linearity of the networks. Average pooling, on the other hand, is sensitive to spatial perturbations, but is a linear function. For semantic segmentation, we prefer both the preservation of detailed cues within a local feature region and non-linearity that increases a network's functional complexity. In this work, we propose a polynomial pooling (P-pooling) function that finds an intermediate form between max and average pooling to provide an optimally balanced and self-adjusted pooling strategy for semantic segmentation. The P-pooling is differentiable and can be applied into a variety of pre-trained networks. Extensive studies on the PASCAL VOC, Cityscapes and ADE20k datasets demonstrate the superiority of P-pooling over other poolings. Experiments on various network architectures and state-of-the-art training strategies also show that models with P-pooling layers consistently outperform those directly fine-tuned using pre-trained classification models.

Recent studies have shown remarkable success in face manipulation task with the advance of GANs and VAEs paradigms, but the outputs are sometimes limited to low-resolution and lack of diversity. In this work, we propose Additive Focal Variational Auto-encoder (AF-VAE), a novel approach that can arbitrarily manipulate high-resolution face images using a simple yet effective model and only weak supervision of reconstruction and KL divergence losses. First, a novel additive Gaussian Mixture assumption is introduced with an unsupervised clustering mechanism in the structural latent space, which endows better disentanglement and boosts multi-modal representation with external memory. Second, to improve the perceptual quality of synthesized results, two simple strategies in architecture design are further tailored and discussed on the behavior of Human Visual System (HVS) for the first time, allowing for fine control over the model complexity and sample quality. Human opinion studies and new state-of-the-art Inception Score (IS) / Frechet Inception Distance (FID) demonstrate the superiority of our approach over existing algorithms, advancing both the fidelity and extremity of face manipulation task.

Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits their performance is the problem of visual polysemy. To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation. Compared to existing methods, the primary advantage of our approach lies in that our approach can adapt to the dynamic changes in the search results. Our proposed framework consists of two major steps: we first discover and dynamically select the text queries according to the image search results, then we employ the proposed saliency-guided deep multi-instance learning network to remove outliers and learn classification models for visual disambiguation. Extensive experiments demonstrate the superiority of our proposed approach.

TV series correlation computing is one of the most important tasks of personalized online streaming services. With the relevance of TV series and viewer feedback, we can calculate the TV series correlation table based on the viewer's implicit feedback which does not perform well for the newly added "cold start" TV series. In this paper, we aim to improve correlation computing within the cold-start phase. We propose a framework named Time-aware Session Embedding (TSE), with Item Embedding in Session and Time Decay Factor for a multimodal recommendation. We apply an lower- dimensional vector as item embedding and calculate their factor considering the time decay. The framework performed well in the Content-based Video Relevance Prediction Challenge and we get the first place in this competition.

In the literature of video analysis, most researches, such as retrieval and recognition, hypothesize that each input video contains at least one complete semantic entity, e.g. an activity, action and event.However, this hypothesis does not hold in many realistic scenarios due to two main reasons. First, complete videos whose qualities are good enough for automatic analysis are not always accessible because of heavy motion blur, occlusions, interruptions, etc. % Second, extracting features from complete videos always fails to meet up with speed and storage requirements in large-scale use cases.To tackle these challenges, incomplete videos are more useful, but researches on them are seldom mentioned. In this paper, we propose a novel and effective hashing framework specialized in large-scale incomplete video analysis called Generative Reconstructive Hashing (GRH). To begin with, an adversarial generative network that is specially designed to map incomplete video features to the feature distributions of complete videos, so that features of incomplete videos become indistinguishable from those of complete videos. Then, the discriminative hashing module further fills the gap between full video features and estimated features from partial videos by projecting both features into a common binary feature space, which allows improvement in efficiency compared with real-value based methods. GRH is the first end-to-end framework for incomplete video analysis. Extensive experiments on various datasets demonstrate GRH's superior effectiveness and efficiency on retrieval and recognition tasks. GRH outperforms the recent state-of-the-art methods by 5.44/3.22/4.82 in terms of MAPs on HMDB51/UCF101/CCV datasets, respectively.

Clustering is a long-standing important research problem, however, remains challenging when handling large-scale image data from diverse sources. In this paper, we present a novel Binary Multi-View Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data. To achieve this goal, we formulate BMVC by two key components: compact collaborative discrete representation learning and binary clustering structure learning, in a joint learning framework. Specifically, BMVC collaboratively encodes the multi-view image descriptors into a compact common binary code space by considering their complementary information; the collaborative binary representations are meanwhile clustered by a binary matrix factorization model, such that the cluster structures are optimized in the Hamming space by pure, extremely fast bit-operations. For efficiency, the code balance constraints are imposed on both binary data representations and cluster centroids. Finally, the resulting optimization problem is solved by an alternating optimization scheme with guaranteed fast convergence. Extensive experiments on four large-scale multi-view image datasets demonstrate that the proposed method enjoys the significant reduction in both computation and memory footprint, while observing superior (in most cases) or very competitive performance, in comparison with state-of-the-art clustering methods.

Visual tracking is one of the fundamental problems in computer vision. Recently, some deep-learning-based tracking algorithms have been illustrating record-breaking performances. However, due to the high complexity of neural networks, most deep trackers suffer from low tracking speed and are, thus, impractical in many real-world applications. Some recently proposed deep trackers with smaller network structure achieve high efficiency while at the cost of significant decrease in precision. In this paper, we propose to transfer the deep feature, which is learned originally for image classification to the visual tracking domain. The domain adaptation is achieved via some “grafted” auxiliary networks, which are trained by regressing the object location in tracking frames. This adaptation improves the tracking performance significantly both on accuracy and efficiency. The yielded deep tracker is real time and also illustrates the state-of-the-art accuracies in the experiment involving two well-adopted benchmarks with more than 100 test videos. Furthermore, the adaptation is also naturally used for introducing the objectness concept into visual tracking. This removes a long-standing target ambiguity in visual tracking tasks, and we illustrate the empirical superiority of the more well-defined task.

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.

Most learning-based hashing algorithms leverage sample-to-sample similarities, such as neighborhood structure, to generate binary codes, which achieve promising results for image retrieval. This type of methods are referred to as instance-level encoding. However, it is nontrivial to define a scalar to represent sample-to-sample similarity encoding the semantic labels and the data structure. To address this issue, in this paper, we seek to use a class-level encoding method, which encodes the class-to-class relationship, to take the semantic information of classes into consideration. Based on these two encodings, we propose a novel framework, error correcting input and output (EC-IO) coding, which does class-level and instance-level encoding under a unified mapping space. Our proposed model contains two major components, which are distribution preservation and error correction. With these two components, our model maps the input feature of samples and the output code of classes into a unified space to encode the intrinsic structure of data and semantic information of classes simultaneously. Under this framework, we present our hashing model, EC-IO hashing (EC-IOH), by approximating the mapping space with the Hamming space. Extensive experiments are conducted to evaluate the retrieval performance, and EC-IOH exhibits superior and competitive performances comparing with popular supervised and unsupervised hashing 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.

The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instancelevel multi-instance learning (MIL) model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark datasets demonstrate the superiority of our proposed approach.

Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time consuming and labor intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this paper, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then, we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and reranking search results demonstrate the superiority of our proposed approach.

To reduce the dependence on labeled data, there have been increasing research efforts on learning visual classifiers by exploiting web images. One issue that limits their performance is the problem of polysemy. To solve this problem, in this work, we present a novel framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses to retrieve sense-specific images. Then we merge visual similar semantic senses and prune noises by using the retrieved images. Finally, we train a visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and re-ranking search results demonstrate the superiority of our proposed approach.

Recent studies show that large-scale sketch-based image retrieval (SBIR) can be efficiently tackled by cross-modal binary representation learning methods, where Hamming distance matching significantly speeds up the process of similarity search. Providing training and test data subjected to a fixed set of pre-defined categories, the cutting-edge SBIR and cross-modal hashing works obtain acceptable retrieval performance. However, most of the existing methods fail when the categories of query sketches have never been seen during training. In this paper, the above problem is briefed as a novel but realistic zero-shot SBIR hashing task. We elaborate the challenges of this special task and accordingly propose a zero-shot sketch-image hashing (ZSIH) model. An end-to-end three-network architecture is built, two of which are treated as the binary encoders. The third network mitigates the sketch-image heterogeneity and enhances the semantic relations among data by utilizing the Kronecker fusion layer and graph convolution, respectively. As an important part of ZSIH, we formulate a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval. To the best of our knowledge, ZSIH is the first zero-shot hashing work suitable for SBIR and cross-modal search. Comprehensive experiments are conducted on two extended datasets, i.e., Sketchy and TU-Berlin with a novel zero-shot train-test split. The proposed model remarkably outperforms related works.

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.

The performance of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), \eg attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. To address this issue, we propose to enhance classifier learning by extracting PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data. In detail, we treat each selected PI as a subcategory and learn one classifier for per subcategory independently. The classifiers for all subcategories are then integrated together to form a more powerful category classifier. Particularly, we propose a new instance-level multi-instance learning (MIL) model to simultaneously select a subset of training images from each subcategory and learn the optimal classifiers based on the selected images. Extensive experiments demonstrate the superiority of our approach.

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.

Depth images have always been playing critical roles for indoor scene understanding problems, and are particularly important for tasks in which 3D inferences are involved. However, since depth images are not universally available, abandoning them from the testing stage can significantly improve the generality of a method. In this work, we consider the scenarios where depth images are not available in the testing data, and propose to learn a convolutional long short-term memory (Conv LSTM) network and a regression convolutional neural network (regression ConvNet) using only monocular RGB images. The proposed networks benefit from 2D segmentations, object-level spatial context, object-scene dependencies and objects' geometric information, where optimization is governed by the semantic label loss, which measures the label consistencies of both objects and scenes, and the 3D geometrical loss, which measures the correctness of objects' 6Dof estimation. Conv LSTM and regression ConvNet are applied to scene/object classification, object detection and 6Dof estimation tasks respectively, where we utilize the joint inference from both networks and further provide the perspective of synthesizing fully rigged 3D scenes according to objects' arrangements in monocular images. Both quantitative and qualitative experimental results are provided on the NYU-v2 dataset, and we demonstrate that the proposed Conv LSTM can achieve state-of-the-art performance without requiring the depth information.

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.

Zero-shot learning (ZSL) aims to recognize unseen classes that are excluded from training classes. ZSL suffers from 1) Zero-shot bias (Z-Bias) --- model is biased towards seen classes because unseen data is inaccessible for training; 2) Zero-shot variance (Z-Variance) --- associating different images to same semantic embedding yields large associating error. To reduce Z-Bias, we propose a pseudo transfer mechanism, where we first synthesize the distribution of unseen data using semantic embeddings, then we minimize the mismatch between the seen distribution and the synthesized unseen distribution. To reduce Z-Variance, we implicitly corrupted one semantic embedding multiple times to generate image-wise semantic vectors, with which our model learn robust classifiers. Lastly, we integrate our Z-Bias and Z-variance reduction techniques with a linear ZSL model to show its usefulness. Our proposed model successfully overcomes the Z-bias and Z-variance problems. Extensive experiments on five benchmark datasets including ImageNet-1K demonstrate that our model outperforms the state-of-the-art methods with fast training.

The past decade has witnessed the boom of human-machine interactions, particularly via dialog systems. In this paper, we study the task of response generation in open-domain multi-turn dialog systems. Many research efforts have been dedicated to building intelligent dialog systems, yet few shed light on deepening or widening the chatting topics in a conversational session, which would attract users to talk more. To this end, this paper presents a novel deep scheme consisting of three channels, namely global, wide, and deep ones. The global channel encodes the complete historical information within the given context, the wide one employs an attention-based recurrent neural network model to predict the keywords that may not appear in the historical context, and the deep one trains a Multi-layer Perceptron model to select some keywords for an in-depth discussion. Thereafter, our scheme integrates the outputs of these three channels to generate desired responses. To justify our model, we conducted extensive experiments to compare our model with several state-of-the-art baselines on two datasets: one is constructed by ourselves and the other is a public benchmark dataset. Experimental results demonstrate that our model yields promising performance by widening or deepening the topics of interest.

Precisely recommending relevant multimedia items from massive candidates to a large number of users is an indispensable yet difficult task on many platforms. A promising way is to project users and items into a latent space and recommend items via the inner product of latent factor vectors. However, previous studies paid little attention to the multimedia content itself and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Content-aware Multimedia Recommendation Model with Graph Autoencoder (GraphCAR), combining informative multimedia content with user-item interaction. Specifically, user-item interaction, user attributes and multimedia contents (e.g., images, videos, audios, etc.) are taken as input of the autoencoder to generate the item preference scores for each user. Through extensive experiments on two real-world multimedia Web services: Amazon and Vine, we show that GraphCAR significantly outperforms state-of-the-art techniques of both collaborative filtering and content-based methods.

In our daily life, how to match clothing well is always a troublesome problem especially when we are shopping online to select a pair of matched pieces of clothing from tens of thousands available selections. To help common customers overcome selection issues, recent studies in the recommender system area have started to infer the fashion matching results automatically. The traditional fashion recommendation is normally achieved by considering visual similarity of clothing items or/and item co-purchase history from existing shopping transactions. Due to the high complexity of visual features and the lack of historical item purchase records, most of the existing work is unlikely to make an efficient and accurate recommendation. To address the problem, in this paper, we propose a new model called Discrete Supervised Fashion Coordinates Hashing. Its main objective is to learn meaningful yet compact high-level features of clothing items, which are represented as binary hash codes. In detail, this learning process is supervised by a clothing matching matrix, which is initially constructed based on limited known matching pairs and subsequently on the self-augmented ones. The proposed model jointly learns the intrinsic matching patterns from the matching matrix and the binary representations from the clothing items’ images, where the visual feature of each clothing item is discretized into a fixed-length binary vector. The binary representation learning significantly reduces the memory cost and accelerates the recommendation speed. The experiments compared with several state-of-the-art approaches have evidenced the superior performance of the proposed approach on efficient fashion recommendation.

Sufficient training examples are the fundamental requirement for most of the learning tasks. However, collecting well-labelled training examples is costly. Inspired by Zero-shot Learning (ZSL) that can make use of visual attributes or natural language semantics as an intermediate level clue to associate low-level features with high-level classes, in a novel extension of this idea, we aim to synthesise training data for novel classes using only semantic attributes. Despite the simplicity of this idea, there are several challenges. First, how to prevent the synthesised data from over-fitting to training classes? Second, how to guarantee the synthesised data is discriminative for ZSL tasks? Third, we observe that only a few dimensions of the learnt features gain high variances whereas most of the remaining dimensions are not informative. Thus, the question is how to make the concentrated information diffuse to most of the dimensions of synthesised data. To address the above issues, we propose a novel embedding algorithm named Unseen Visual Data Synthesis (UVDS) that projects semantic features to the high-dimensional visual feature space. Two main techniques are introduced in our proposed algorithm. (1) We introduce a latent embedding space which aims to reconcile the structural difference between the visual and semantic spaces, meanwhile preserve the local structure. (2) We propose a novel Diffusion Regularisation (DR) that explicitly forces the variances to diffuse over most dimensions of the synthesised data. By an orthogonal rotation (more precisely, an orthogonal transformation), DR can remove the redundant correlated attributes and further alleviate the over-fitting problem. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data for zero-shot learning. Extensive experimental results suggest that our proposed approach significantly outperforms the state-of-the-art methods.

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.

Recognition and detection of interactive actions performed by multiple persons have a wide range of real-world applications. Existing studies on the human activity analysis focus mainly on classifying video clips of simple actions performed by a single person, whereas the problem of understanding complex human activities with causal relationships between two people has not been sufficiently addressed yet. In this paper, we employ systematically organized skeleton features enhanced with directional features, and utilize sparse-group lasso to automatically choose discriminative factors that help in dealing with interactive action recognition and real-time detection tasks. Experiments on two person interaction datasets demonstrate the superiority of our approach to the state-of-the-art methods.

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.

Recently, hash learning attracts great attentions since it can obtain fast image retrieval on large-scale datasets by using a series of discriminative binary codes. The popular methods include manifold-based hashing methods, which aim to learn the binary codes by embedding the original high-dimensional data into low-dimensional intrinsic subspace. However, most of these methods tend to relax the discrete constraint to compute the final binary codes in an easier way. Therefore, the information loss will increase. In this paper, we propose a novel jointly sparse regression model to minimize the locality information loss and obtain jointly sparse hashing method. The proposed model integrates locality, joint sparsity and rotation operation together with a seamless formulation. Thus, the drawback in previous methods using two separated and independent stages such as PCA-ITQ and the similar methods can be addressed. Moreover, since we introduce the joint sparsity, the feature extraction and jointly sparse feature selection can also be realized in a single projection operation, which has the potentials to select more discriminant features. The convergence of the proposed algorithm is proved, and the essences of the iterative procedures are also revealed. The experimental results on large-scale datasets demonstrate the performance of the proposed method.

Hashing techniques have recently gained increasing research interest in multimedia studies. Most existing hashing methods only employ single features for hash code learning. Multiview data with each view corresponding to a type of feature generally provides more comprehensive information. How to efficiently integrate multiple views for learning compact hash codes still remains challenging. In this article, we propose a novel unsupervised hashing method, dubbed multiview discrete hashing (MvDH), by effectively exploring multiview data. Specifically, MvDH performs matrix factorization to generate the hash codes as the latent representations shared by multiple views, during which spectral clustering is performed simultaneously. The joint learning of hash codes and cluster labels enables that MvDH can generate more discriminative hash codes, which are optimal for classification. An efficient alternating algorithm is developed to solve the proposed optimization problem with guaranteed convergence and low computational complexity. The binary codes are optimized via the discrete cyclic coordinate descent (DCC) method to reduce the quantization errors. Extensive experimental results on three large-scale benchmark datasets demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of both accuracy and scalability.

Large-scale clustering has been widely used in many applications, and has received much attention. Most existing clustering methods suffer from both expensive computation and memory costs when applied to large-scale datasets. In this paper, we propose a novel clustering method, dubbed compressed k-means (CKM), for fast large-scale clustering. Specifically, high-dimensional data are compressed into short binary codes, which are well suited for fast clustering. CKM enjoys two key benefits: 1) storage can be significantly reduced by representing data points as binary codes; 2) distance computation is very efficient using Hamming metric between binary codes. We propose to jointly learn binary codes and clusters within one framework. Extensive experimental results on four large-scale datasets, including two million-scale datasets demonstrate that CKM outperforms the state-of-the-art large-scale clustering methods in terms of both computation and memory cost, while achieving comparable clustering accuracy.

Recently, a new type of video understanding task called Movie-Fill-in-the-Blank (MovieFIB) has attracted many research attentions. Given a pair of movie clip and description with one blank word as input, MovieFIB aims to automatically predict the blank word. Because of the advantage in processing sequence data, Long-Short Term Memory (LSTM) has been used as a key component in existing MovieFIB methods to generate representations of videos and descriptions. However, most of these methods fail to emphasize the salient parts of videos. To address this problem, in this paper we propose to use a novel LSTM network called LSTM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB. Specifically, we first use LSTM to produce video features, which are then used to update the text representation. Finally, we put the updated text into two opposite directional LSTMwL layers to infer the blank word. Experimental results demonstrate that our approach outperforms state-of-the-art models for MovieFIB.

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images. Work in this area mainly focuses on extracting representative and shared features for sketches and natural images. However, these can neither cope well with the geometric distortion between sketches and images nor be feasible for large-scale SBIR due to the heavy continuous-valued distance computation. In this paper, we speed up SBIR by introducing a novel binary coding method, named Deep Sketch Hashing (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework. Specifically, three convolutional neural networks are utilized to encode free-hand sketches, natural images and, especially, the auxiliary sketch-tokens which are adopted as bridges to mitigate the sketch-image geometric distortion. The learned DSH codes can effectively capture the cross-view similarities as well as the intrinsic semantic correlations between different categories. To the best of our knowledge, DSH is the first hashing work specifically designed for category-level SBIR with an end-to-end deep architecture. The proposed DSH is comprehensively evaluated on two large-scale datasets of TU-Berlin Extension and Sketchy, and the experiments consistently show DSHs superior SBIR accuracies over several state-of-the-art methods, while achieving significantly reduced retrieval time and memory footprint.

Learning to hash has been recognized to accomplish highly efficient storage and retrieval for large-scale visual data. Particularly, ranking-based hashing techniques have recently attracted broad research attention because ranking accuracy among the retrieved data is well explored and their objective is more applicable to realistic search tasks. However, directly optimizing discrete hash codes without continuous-relaxations on a nonlinear ranking objective is infeasible by either traditional optimization methods or even recent discrete hashing algorithms. To address this challenging issue, in this paper, we introduce a novel supervised hashing method, dubbed Discrete Semantic Ranking Hashing (DSeRH), which aims to directly embed semantic rank orders into binary codes. In DSeRH, a generalized Adaptive Discrete Minimization (ADM) approach is proposed to discretely optimize binary codes with the quadratic nonlinear ranking objective in an iterative manner and is guaranteed to converge quickly. Additionally, instead of using 0/1 independent labels to form rank orders as in previous works, we generate the listwise rank orders from the high-level semantic word embeddings which can quantitatively capture the intrinsic correlation between different categories. We evaluate our DSeRH, coupled with both linear and deep convolutional neural network (CNN) hash functions, on three image datasets, i.e., CIFAR-10, SUN397 and ImageNet100, and the results manifest that DSeRH can outperform the state-of-the-art ranking-based hashing methods.

Robust object recognition systems usually rely on powerful feature extraction mechanisms from a large number of real images. However, in many realistic applications, collecting sufficient images for ever-growing new classes is unattainable. In this paper, we propose a new Zero-shot learning (ZSL) framework that can synthesise visual features for unseen classes without acquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage. Hereafter, ZSL recognition is converted into the conventional supervised problem, i.e. the synthesised visual features can be straightforwardly fed to typical classifiers such as SVM. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data. Extensive experimental results manifest that our proposed approach significantly improve the state-of-the-art results.

Traditional action recognition methods aim to recognize actions with complete observations/executions. However, it is often difficult to capture fully executed actions due to occlusions, interruptions, etc. Meanwhile, action prediction/recognition in advance based on partial observations is essential for preventing the situation from deteriorating. Besides, fast spotting human activities using partially observed data is a critical ingredient for retrieval systems. Inspired by the recent success of data binarization in efficient retrieval/recognition, we propose a novel approach, named Partial Reconstructive Binary Coding (PRBC), for action analysis based on limited frame glimpses during any period of the complete execution. Specifically, we learn discriminative compact binary codes for partial actions via a joint learning framework, which collaboratively tackles feature reconstruction as well as binary coding. We obtain the solution to PRBC based on a discrete alternating iteration algorithm. Extensive experiments on four realistic action datasets in terms of three tasks (i.e., partial action retrieval, recognition and prediction) clearly show the superiority of PRBC over the state-of-the-art methods, along with significantly reduced memory load and computational costs during the online test.

Recently, zero-shot action recognition (ZSAR) has emerged with the explosive growth of action categories. In this paper, we explore ZSAR from a novel perspective by adopting the Error-Correcting Output Codes (dubbed ZSECOC). Our ZSECOC equips the conventional ECOC with the additional capability of ZSAR, by addressing the domain shift problem. In particular, we learn discriminative ZSECOC for seen categories from both category-level semantics and intrinsic data structures. This procedure deals with domain shift implicitly by transferring the well-established correlations among seen categories to unseen ones. Moreover, a simple semantic transfer strategy is developed for explicitly transforming the learned embeddings of seen categories to better fit the underlying structure of unseen categories. As a consequence, our ZSECOC inherits the promising characteristics from ECOC as well as overcomes domain shift, making it more discriminative for ZSAR. We systematically evaluate ZSECOC on three realistic action benchmarks, i.e. Olympic Sports, HMDB51 and UCF101. The experimental results clearly show the superiority of ZSECOC over the state-of-the-art methods.

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.

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.

Visual tracking is a fundamental problem in computer vision. Recently, some deep-learning-based tracking algorithms have been achieving record-breaking performances. However, due to the high complexity of deep learning, most deep trackers suffer from low tracking speed, and thus are impractical in many real-world applications. Some new deep trackers with smaller network structure achieve high efficiency while at the cost of significant decrease on precision. In this paper, we propose to transfer the feature for image classification to the visual tracking domain via convolutional channel reductions. The channel reduction could be simply viewed as an additional convolutional layer with the specific task. It not only extracts useful information for object tracking but also significantly increases the tracking speed. To better accommodate the useful feature of the target in different scales, the adaptation filters are designed with different sizes. The yielded visual tracker is real-time and also illustrates the state-of-the-art accuracies in the experiment involving two well-adopted benchmarks with more than 100 test videos.

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.

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.

Anomaly detection and localization in surveillance videos have attracted broad attention in both academic and industry for its importance to public safety, which however remain challenging. In this demonstration, we propose an anomaly detection algorithm called 2stream-VAE/GAN by embedding VAE/GAN in a two-stream architecture. By taking both spatial and temporal information into consideration, normality can be captured and anomaly detection can be achieved. With an outlier detection rule, the system automatically locates anomaly based on a pre-trained model, which suits well for both streaming and local videos.

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.

Travel route planning aims to mine user's attributes and recommend personalized routes. How to build interest model for users and understand their real intention brings great challenges. This paper presents an approach which mines the user interest model by multi-source social media (e.g., travelogues and check-in records), and understands the user's real intention by active behavior such as point of interest (POI) inputs. In order to unify heterogeneous data from different sources, a topical package is built as the measurement space. Based on the topical package, user topical package is modeled to find user interest and route topical package is constructed to describe the attributes of each route. User's active behavior can also be considered during route planning, where top ranked routes are finally recommended. The proposed multi-source topical package (MSTP) approach is evaluated on a real dataset and compared with two state-of-the-art methods. The result shows that MSTP performs better for providing personalized travel routes.

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.

With the dramatic development of the Internet, how to exploit large-scale retrieval techniques for multimodal web data has become one of the most popular but challenging problems in computer vision and multimedia. Recently, hashing methods are used for fast nearest neighbor search in large-scale data spaces, by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. Inspired by this, in this paper, we introduce a novel supervised cross-modality hashing framework, which can generate unified binary codes for instances represented in different modalities. Particularly, in the learning phase, each bit of a code can be sequentially learned with a discrete optimization scheme that jointly minimizes its empirical loss based on a boosting strategy. In a bitwise manner, hash functions are then learned for each modality, mapping the corresponding representations into unified hash codes. We regard this approach as cross-modality sequential discrete hashing (CSDH), which can effectively reduce the quantization errors arisen in the oversimplified rounding-off step and thus lead to high-quality binary codes. In the test phase, a simple fusion scheme is utilized to generate a unified hash code for final retrieval by merging the predicted hashing results of an unseen instance from different modalities. The proposed CSDH has been systematically evaluated on three standard data sets: Wiki, MIRFlickr, and NUS-WIDE, and the results show that our method significantly outperforms the state-of-the-art multimodality hashing techniques.

We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical inter-class structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. The experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification.

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.

Product quantization (PQ) has been recognized as a useful technique to encode visual feature vectors into compact codes to reduce both the storage and computation cost. Recent advances in retrieval and vision tasks indicate that high-dimensional descriptors are critical to ensuring high accuracy on large-scale data sets. However, optimizing PQ codes with high-dimensional data is extremely time-consuming and memory-consuming. To solve this problem, in this paper, we present a novel PQ method based on bilinear projection, which can well exploit the natural data structure and reduce the computational complexity. Specifically, we learn a global bilinear projection for PQ, where we provide both non-parametric and parametric solutions. The non-parametric solution does not need any data distribution assumption. The parametric solution can avoid the problem of local optima caused by random initialization, and enjoys a theoretical error bound. Besides, we further extend this approach by learning locally bilinear projections to fit underlying data distributions. We show by extensive experiments that our proposed method, dubbed bilinear optimization product quantization, achieves competitive retrieval and classification accuracies while having significant lower time and space complexities.

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.

Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time-consuming and labor intensive. To reduce the cost of manual labeling, there has been increased research interest in automatically constructing image datasets by exploiting web images. Datasets constructed by existing methods tend to have a weak domain adaptation ability, which is known as the “dataset bias problem.” To address this issue, we present a novel image dataset construction framework that can be generalized well to unseen target domains. Specifically, the given queries are first expanded by searching the Google Books Ngrams Corpus to obtain a rich semantic description, from which the visually nonsalient and less relevant expansions are filtered out. By treating each selected expansion as a “bag” and the retrieved images as “instances,” image selection can be formulated as a multi-instance learning problem with constrained positive bags. We propose to solve the employed problems by the cutting-plane and concave-convex procedure algorithm. By using this approach, images from different distributions can be kept while noisy images are filtered out. To verify the effectiveness of our proposed approach, we build an image dataset with 20 categories. Extensive experiments on image classification, cross-dataset generalization, diversity comparison, and object detection demonstrate the domain robustness of our dataset.

Many real applications in real-time news stream advertising call for efficient processing of long queries against short text. In such applications, dynamic news feeds are regarded as queries to match against an advertisement (ad) database for retrieving the k most relevant ads. The existing approaches to keyword retrieval cannot work well in this search scenario when queries are triggered at a very high frequency. To address the problem, we introduce new techniques to significantly improve search performance. First, we devise a two-level partitioning for tight upper bound estimation and a lazy evaluation scheme to delay full evaluation of unpromising candidates, which can bring three to four times performance boosting in a database with 7 million ads. Second, we propose a novel rank-aware block-oriented inverted index to further improve performance. In this index scheme, each entry in an inverted list is assigned a rank according to its importance in the ad. Then, we introduce a block-at-a-time search strategy based on the index scheme to support a much tighter upper bound estimation and a very early termination. We have conducted experiments with real datasets, and the results show that the rank-aware method can further improve performance by an order of magnitude.

The goal of this work is to automatically collect a large number of highly relevant images from the Internet for given queries. A novel image dataset construction framework is proposed by employing multiple textual metadata. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora to obtain a richer semantic description, from which the visually non-salient and less relevant expansions are then filtered. After retrieving images from the Internet with filtered expansions, we further filter noisy images by clustering and progressively Convolutional Neural Networks (CNN). To verify the effectiveness of our proposed method, we construct a dataset with 10 categories, which is not only much larger than but also have comparable cross-dataset generalization ability with manually labeled dataset STL-10 and CIFAR-10.

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.

Video segmentation has become an important and active research area with a large diversity of proposed approaches. Graph-based methods, enabling top performance on recent benchmarks, usually focus on either obtaining a precise similarity graph or designing efficient graph cutting strategies. However, these two components are often conducted in two separated steps, and thus the obtained similarity graph may not be the optimal one for segmentation and this may lead to suboptimal results. In this paper, we propose a novel framework, joint graph learning and video segmentation (JGLVS)}, which learns the similarity graph and video segmentation simultaneously. JGLVS learns the similarity graph by assigning adaptive neighbors for each vertex based on multiple cues (appearance, motion, boundary and spatial information). Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the similarity graph, such that the connected components in the resulted similarity graph are exactly equal to the number of segmentations. Furthermore, JGLVS can automatically weigh multiple cues and calibrate the pairwise distance of superpixels based on their topology structures. Most noticeably, empirical results on the challenging dataset VSB100 show that JGLVS achieves promising performance on the benchmark dataset which outperforms the state-of-the-art by up to 11% for the BPR metric.

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.

There have been increasing research interests in automatically constructing image dataset by collecting images from the Internet. However, existing methods tend to have a weak domain adaptation ability, known as the "dataset bias problem". To address this issue, in this work, we propose a novel image dataset construction framework which can generalize well to unseen target domains. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora (GBNC) to obtain a richer semantic description, from which the noisy query expansions are then filtered out. By treating each expansion as a "bag" and the retrieved images therein as "instances", we formulate image filtering as a multi-instance learning (MIL) problem with constrained positive bags. By this approach, images from different data distributions will be kept while with noisy images filtered out. Comprehensive experiments on two challenging tasks demonstrate the effectiveness of our proposed approach.

We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. However, existing hashing methods for CF employ binary code learning procedures that most suffer from the challenging discrete constraints. Hence, those methods generally adopt a two-stage learning scheme composed of relaxed optimization via discarding the discrete constraints, followed by binary quantization. We argue that such a scheme will result in a large quantization loss, which especially compromises the performance of large-scale CF that resorts to longer binary codes. In this paper, we propose a principled CF hashing framework called Discrete Collaborative Filtering (DCF), which directly tackles the challenging discrete optimization that should have been treated adequately in hashing. The formulation of DCF has two advantages: 1) the Hamming similarity induced loss that preserves the intrinsic user-item similarity, and 2) the balanced and uncorrelated code constraints that yield compact yet informative binary codes. We devise a computationally efficient algorithm with a rigorous convergence proof of DCF. Through extensive experiments on several real-world benchmarks, we show that DCF consistently outperforms state-of-the-art CF hashing techniques, e.g, though using only 8 bits, DCF is even significantly better than other methods using 128 bits.

Hashing techniques have been widely applied to large-scale cross-view retrieval tasks due to the significant advantage of binary codes in computation and storage efficiency. However, most existing cross-view hashing methods learn binary codes with continuous relaxations, which cause large quantization loss across views. To address this problem, in this letter, we propose a novel cross-view hashing method, where a common Hamming space is learned such that binary codes from different views are consistent and comparable. The quantization loss across views is explicitly reduced by two carefully designed regression terms from original spaces to the Hamming space. In our method, the l2,1-norm regularization is further exploited for discriminative feature selection. To obtain high-quality binary codes, we propose to jointly learn the codes and hash functions, for which an efficient iterative algorithm is presented. We evaluate the proposed method, dubbed Robust Cross-view Hashing (RCH), on two benchmark datasets and the results demonstrate the superiority of RCH over many other state-of-the-art methods in terms of retrieval performance and cross-view consistency.

Decomposing target into several parts may improve the capability of tracking algorithm to deal with appearance variations such as occlusion and deformation. In this letter, we propose a part-based appearance model by exploiting spatial structure from parts. The model minimizes appearance and deformation cost simultaneously to predict the new position of object. Then, the optimization problem is divided into two parts. Kernelized correlation filter (KCF) is used for tracking the appearance of parts separately to speed up the proposed tracker. Meanwhile, the deformation cost is minimized by structural learning schema, which can reduce the label noise that caused by inaccuracy bounding box. Finally, minimum spanning tree and dynamic programming are employed to combine the score map of the appearance and deformation of parts, and to detect best new position of target. Experimental results on several challenge sequences show the efficiency and effectiveness of the proposed tracking algorithm.

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.

Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for high-dimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the pursued hash codes, which typically makes hash optimizations very challenging (NP-hard in general). In this work, we propose a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm. One of the key steps in this algorithm is to solve a regularization sub-problem associated with the NP-hard binary optimization. We show that the sub-problem admits an analytical solution via cyclic coordinate descent. As such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, therefore enabling to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the state-of-the-art hashing methods in large-scale image retrieval.

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.

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.

Hashing techniques have attracted broad research interests in recent multimedia studies. However, most of existing hashing methods focus on learning binary codes from data with only one single view, and thus cannot fully utilize the rich information from multiple views of data. In this paper, we propose a novel unsupervised hashing approach, dubbed multi-view latent hashing (MVLH), to effectively incorporate multi-view data into hash code learning. Specifically, the binary codes are learned by the latent factors shared by multiple views from an unified kernel feature space, where the weights of different views are adaptively learned according to the reconstruction error with each view. We then propose to solve the associate optimization problem with an efficient alternating algorithm. To obtain high-quality binary codes, we provide a novel scheme to directly learn the codes without resorting to continuous relaxations, where each bit is efficiently computed in a closed form. We evaluate the proposed method on several large-scale datasets and the results demonstrate the superiority of our method over several other state-of-the-art methods.

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.

Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels. To apply deep hashing on datasets without labels, we propose a deep self-taught hashing algorithm (DSTH), which generates a set of pseudo labels by analyzing the data itself, and then learns the hash functions for novel data using discriminative deep models. Furthermore, we generalize DSTH to support both supervised and unsupervised cases by adaptively incorporating label information. We use two different deep learning framework to train the hash functions to deal with out-of-sample problem and reduce the time complexity without loss of accuracy. We have conducted extensive experiments to investigate different settings of DSTH, and compared it with state-of-the-art counterparts in six publicly available datasets. The experimental results show that DSTH outperforms the others in all datasets.

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.

Learning-based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes preserving the Euclidean similarity in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexities of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this paper, how to learn compact binary embeddings on their intrinsic manifolds is considered. In order to address the above-mentioned difficulties, an efficient, inductive solution to the out-of-sample data problem, and a process by which nonparametric manifold learning may be used as the basis of a hashing method are proposed. The proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. It is particularly shown that hashing on the basis of t-distributed stochastic neighbor embedding outperforms state-of-the-art hashing methods on large-scale benchmark data sets, and is very effective for image classification with very short code lengths. It is shown that the proposed framework can be further improved, for example, by minimizing the quantization error with learned orthogonal rotations without much computation overhead. In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.

Boosting algorithms, especially AdaBoost, have attracted great attention in computer vision. In the early version of boosting algorithms, the weak classifier selection and the strong classifier learning are linked together. It has been demonstrated that decoupling of these two processes can provide more flexibility for training a better classifier. In these studies, linear discriminant analysis (LDA) has been adopted to select weak classifiers independently based on class separability rather than a training error that occurs normally in AdaBoost. It is observed that LDA is successful only if a large number of labeled training samples is available. However, a large-scale labeled training set is not always available in many computer vision applications such as object classification. To tackle this problem, this paper proposes semisupervised subspace learning combined with a boosting framework for object classification, through which unlabeled data can participate in the boosting training to compensate for the lack of enough labeled data. With the proposed framework, this paper develops three various approaches that utilize unlabeled data in different ways. According to the experiments on several public image data sets, the proposed methods achieve superior performance over AdaBoost and existing semisupervised algorithms.

Learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes that preserve the Euclidean distance in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexity of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE [29] outperforms state-of-the-art hashing methods on large-scale benchmark datasets, and is very effective for image classification with very short code lengths.

Boosting algorithms have attracted great attention since the first real-time face detector by Viola & Jones through feature selection and strong classifier learning simultaneously. On the other hand, researchers have proposed to decouple such two procedures to improve the performance of Boosting algorithms. Motivated by this, we propose a boosting-like algorithm framework by embedding semi-supervised subspace learning methods. It selects weak classifiers based on class-separability. Combination weights of selected weak classifiers can be obtained by subspace learning. Three typical algorithms are proposed under this framework and evaluated on public data sets. As shown by our experimental results, the proposed methods obtain superior performances over their supervised counterparts and AdaBoost.

The least trimmed sum of squares (LTS) regression estimation criterion is a robust statistical method for model fitting in the presence of outliers. Compared with the classical least squares estimator, which uses the entire data set for regression and is consequently sensitive to outliers, LTS identifies the outliers and fits to the remaining data points for improved accuracy. Exactly solving an LTS problem is NP-hard, but as we show here, LTS can be formulated as a concave minimization problem. Since it is usually tractable to globally solve a convex minimization or concave maximization problem in polynomial time, inspired by , we instead solve LTS' approximate complementary problem, which is convex minimization. We show that this complementary problem can be efficiently solved as a second order cone program. We thus propose an iterative procedure to approximately solve the original LTS problem. Our extensive experiments demonstrate that the proposed method is robust, efficient and scalable in dealing with problems where data are contaminated with outliers. We show several applications of our method in image analysis.

It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.