Video large language models have achieved remarkable performance in tasks such as video question answering, however, their temporal understanding remains suboptimal. To address this limitation, we curate a dedicated instruction fine-tuning dataset that focuses on enhancing temporal comprehension across five key dimensions. In order to reduce reliance on costly temporal annotations, we introduce a multi-task prompt fine-tuning approach that seamlessly integrates temporal-sensitive tasks into existing instruction datasets without requiring additional annotations. Furthermore, we develop a novel benchmark for temporal-sensitive video understanding that not only fills the gaps in dimension coverage left by existing benchmarks but also rigorously filters out potential shortcuts, ensuring a more accurate evaluation. Extensive experimental results demonstrate that our approach significantly enhances the temporal understanding of video-LLMs while avoiding reliance on shortcuts.

Low-Rank Adaptation (LoRA) has emerged as a powerful parameter-efficient fine-tuning method for adapting large language models to downstream tasks. Recent studies have leveraged Mixture-of-Experts (MoE) mechanism to effectively integrate multiple LoRA modules, facilitating efficient parameter adaptation for multi-task scenarios. It has been shown that fostering knowledge sharing across LoRA experts can greatly enhance parameter adaptation efficiency. However, the existing approach for LoRA expert knowledge sharing still faces two key limitations: constrained functional specialization and induced expert homogenization. To address these issues, we propose a novel diversity-regulated asymmetric MoE-LoRA decomposition framework, which achieves flexible knowledge sharing through asymmetric expert decomposition and guarantees expert diversity with a dual orthogonality regularization. Extensive experiments on eight public benchmarks, spanning both multi-task and single-task settings, demonstrate the superiority of our approach over existing methods.

Composed Image Retrieval (CIR) represents a challenging retrieval task that targets locating specific images through multimodal inputs. Despite recent progress in CIR techniques, prior approaches often overlook cases where images appear visually alike yet differ in attributes, potentially undermining both multimodal feature fusion and similarity modeling. To mitigate this limitation, we design a unified representation of cross-modal features based on attribute prototypes. Nevertheless, the task is far from straightforward, owing to three core issues: (1) entanglement in attribute-level semantics, (2) inconsistency across modalities, and (3) supervised signal missing. To tackle the above obstacles, we introduce a COMposed image retrieval network guided By attrIbute-based NEighbor Relations (COMBINER). Specifically, we first design an Adaptive Semantic Disentanglement module, which is capable of disentangling attribute features based on multimodal primitive features. Secondly, we propose a Unified Prototype-based Composition module, which can construct cross-modal unified prototypes (CUP) and facilitate multimodal feature composition. Finally, we introduce a Dual Relations Modeling module, which can mine pairwise and neighbor relations based on attribute similarity. Compared to traditional neighbor relations modeling CIR methods, COMBINER represents the first study addressing the phenomenon of visually similar but attribute-unrelated samples. It achieves a more accurate understanding of the semantic relations among samples by employing an attribute prototype-based similarity metric. Comprehensive experiments conducted on three benchmark datasets confirm the effectiveness of our proposed COMBINER. The implementation of our method will be accessed at https://github.com/Lee-zixu/COMBINER.

Composed Image Retrieval (CIR) is an emerging yet challenging task that allows users to search for target images using a multimodal query, comprising a reference image and a modification text specifying the user’s desired changes to the reference image. Given its significant academic and practical value, CIR has become a rapidly growing area of interest in the computer vision and machine learning communities, particularly with the advances in deep learning. To the best of our knowledge, there is currently no comprehensive review of CIR to provide a timely overview of this field. Therefore, we synthesize insights from over 150 publications in top conferences and journals, including ACM TOIS, SIGIR, and CVPR. In particular, we systematically categorize existing supervised CIR and zero-shot CIR models using a fine-grained taxonomy. For a comprehensive review, we also briefly discuss approaches for tasks closely related to CIR, such as attribute-based CIR and dialog-based CIR. Additionally, we summarize benchmark datasets for evaluation and analyze existing supervised and zero-shot CIR methods by comparing experimental results across multiple datasets. Furthermore, we present promising future directions in this field, offering practical insights for researchers interested in further exploration.

Fashion vision-language pre-training (VLP) models have demonstrated remarkable capabilities in excelling at a wide range of fashion cross-modal tasks. However, current models still face three notable limitations (1) an inability to discern varying levels of consistency between textual descriptions and multi-view images, (2) a deficiency in explicit fine-grained alignment between images and text, and (3) a lack of specific supervision mechanisms for facilitating global joint embedding learning. To address these limitations, we propose a novel dual alignment-enhanced fashion VLP model. This model delves deeply into the rich resources of multi-view images and semantic attributes associated with each fashion item. Notably, we introduce two novel pre-training tasks: Multi-grained Adaptive Image-Text Alignment (MAITA) and Joint Embedding-oriented Alignment (JEA). MAITA focuses on optimizing the text/image encoder by orchestrating adaptive alignment between multi-view images and input text. This encompasses both coarse-grained and fine-grained alignment strategies to enrich semantic understanding, while JEA is devised to supervise the fine-grained semantic learning process of the global multimodal joint embedding. Experimental results spanning four diverse downstream tasks, including cross-modal retrieval, text-guided image retrieval, category recognition, and subcategory recognition, substantiate the significant performance superiority of our model over prior state-of-the-art fashion VLP models.

Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classification task. In this paper, we experimentally analyze that existing transformer-based MTM methods encounter with two under-explored issues when dealing with time series data: (1) they encode features by performing long-dependency ensemble averaging, which easily results in rank collapse and feature homogenization as the layer goes deeper; (2) they exhibit distinct priorities in fitting different frequency components contained in the time-series, inevitably leading to spectrum energy imbalance of encoded feature. To tackle these issues, we propose an auxiliary content-aware balanced decoder (CBD) to optimize the encoding quality in the spectrum space within masked modeling scheme. Specifically, the CBD iterates on a series of fundamental blocks, and thanks to two tailored units, each block could progressively refine the masked representation via adjusting the interaction pattern based on local content variations of time-series and learning to recalibrate the energy distribution across different frequency components. Moreover, dual-constraint loss is devised to enhance the mutual optimization of vanilla decoder and our CBD. Extensive experimental results on ten time-series classification datasets show that our method nearly surpasses a bunch of baselines. Meanwhile, a series of explanatory results are showcased to sufficiently demystify the behaviors of our method.

Recent studies have focused on introducing pre-trained foundation models into semi-supervised learning (SSL) tasks. Nevertheless, these foundation models can exhibit biases toward different classes and tend to generate imbalanced pseudo-labels for SSL. Thus, efforts have been made to introduce the logit adjustment offset to reduce the inherent bias in foundation models for SSL tasks. Despite their success, existing foundation model-based SSL methods face challenges: 1) unreliability in the estimated logit adjustment offset, 2) overlooking the potential of linguistic knowledge in capturing model biases and 3) fail to fully exploit the unlabeled samples. To address these issues, we propose Language-Assisted Debiasing and Smoothing framework, namely LADaS, for foundation model-based SSL. It consists of two components: 1) Language-assisted Pseudo-Label Debiasing (LPLD) to reduce biases in foundation models, and 2) Language-aware Pseudo-Label Smoothing (LPLS) to fully exploit low-confidence samples to facilitate SSL training. In particular, LPLD introduces a reliability score to dynamically assess the reliability of the logit adjustment. Additionally, it incorporates a language-oriented preference to reduce model biases using linguistic knowledge derived from pre-trained language models. Finally, LPLS introduces language-aware soft pseudo-labels and devises language-aware pseudo-label smoothing loss to guide the learning of unlabeled samples with low-quality pseudo-labels. Extensive experiments demonstrate the superiority of our proposed LADaS.

Composed Image Retrieval (CIR) is a novel image retrieval paradigm that aims at searching for the target images via the multimodal query including a reference image and a modification text. Although existing works have made significant progress, they overlook the inter-modal coherence and incoherence relations modeling, hindering the retrieval accuracy of CIR models. This limitation is non-trivial due to the following two challenges: 1) inter-modal incoherence and 2) intra-modal entanglement. To address the above challenges, we propose a comPlementArity-guided dIsentanglement netwoRk (PAIR), which can disentangle the features of multimodal queries from a semantic coherence perspective, thereby facilitating the identification of both complementary coherent and incoherent features. Furthermore, based on disentangled features, PAIR develops an asymmetric feature composition module, which is designed to enhance the retrieval performance of the model. Extensive experiments on three benchmark datasets demonstrate the superiority of PAIR. The code is available at https://zhihfu.github.io/PAIR.github.io/.

The Composed Image Retrieval (CIR) task aims to retrieve a target image that meets the requirements based on a given multimodal query (includes a reference image and modification text). Most existing works align multimodal semantics at both local and global granularity. However, they have failed to consider the mining of semantic correspondences at the intermediate-grained level, which has resulted in sub-optimal model performance. In this paper, we propose an adaptive interMEDiate-graIned Aggregation Network (MEDIAN). Compared with the conventional CIR models, MEDIAN is capable of generating intermediate-grained feature aggregation supervised signals and constructing graph attention networks to extract intermediate-grained features. Concurrently, MEDIAN also devises cross-modal semantic correspondence aligning guided by the target image, which in turn enables accurate multi-grained feature composition. The superiority of MEDIAN is demonstrated by extensive experiments on three benchmark datasets. Our code is available at https://windlikeo.github.io/MEDIAN.github.io/.

Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image with a multimodal query, i.e., a reference image, and its complementary modification text. As previous supervised or zero-shot learning paradigms all fail to strike a good trade-off between the model’s generalization ability and retrieval performance, recent researchers have introduced the task of few-shot CIR (FS-CIR) and proposed a textual inversion-based network based on pretrained CLIP model to realize it. Despite its promising performance, the approach encounters two key limitations: simply relying on the few annotated samples for CIR model training and indiscriminately selecting training triplets for CIR model fine-tuning. To address these two limitations, we propose a novel two-stage pseudo triplet guided few-shot CIR scheme, dubbed PTG-FSCIR. In the first stage, we propose an attentive masking and captioning-based pseudo triplet generation method, to construct pseudo triplets from pure image data and use them to fulfill the CIR-task specific pretraining. In the second stage, we propose a challenging triplet-based CIR fine-tuning method, where we design a pseudo modification text-based sample challenging score estimation strategy and a robust top range-based random sampling strategy for sampling robust challenging triplets to promote the model fine-tuning. Notably, our scheme is plug-and-play and compatible with any existing supervised CIR models. We test our scheme across two backbones on three public datasets (i.e., FashionIQ, CIRR, and Birds-to-Words), achieving maximum improvements of 13.3%, 22.2%, and 17.4% respectively, demonstrating our scheme’s efficacy.

Current knowledge distillation methods for semantic segmentation are primarily designed for knowledge transfer within homogeneous networks, and are less effective for heterogeneous networks. The feature information output by heterogeneous networks faces several challenges, including differences in feature scales and varying capabilities to represent local and global contextual information. To address these issues, we propose a novel Heterogeneous Model Knowledge Distillation (HMKD) framework using a dual alignment method to improve distillation performance between CNN-based and Transformer-based semantic segmentation models. Specifically, we introduce the Patch-based Self-attention Alignment Module (PSAM), which computes and aligns patch-level self-attention across distinct feature map spaces, enabling the transfer of local or global contextual information between heterogeneous models. Additionally, the Heterogeneous Scale Alignment Module (HSAM) is designed to ensure consistency across heterogeneous feature scales and enrich the semantic content. We have also conducted extensive experiments on two benchmark datasets (Cityscapes and CamVid) to validate the effectiveness and superiority of our approach compared with several recent state-of-the-art (SOTA) methods. Our code is deposited at https://github.com/xumingzhu989/HMKD-ICMR.

Composed Image Retrieval (CIR) represents a novel retrieval paradigm that is capable of expressing users' intricate retrieval requirements flexibly. It enables the user to give a multimodal query, comprising a reference image and a modification text, and subsequently retrieve the target image. Notwithstanding the considerable advances made by prevailing methodologies, CIR remains in its nascent stages due to two limitations: 1) inhomogeneity between dominant and noisy portions in visual data is ignored, leading to query feature degradation, and 2) the priority of textual data in the image modification process is overlooked, which leads to a visual focus bias. To address these two limitations, this work presents a focus mapping-based feature extractor, which consists of two modules: dominant portion segmentation and dual focus mapping. It is designed to identify significant dominant portions in images and guide the extraction of visual and textual data features, thereby reducing the impact of noise interference. Subsequently, we propose a textually guided focus revision module, which can utilize the modification requirements implied in the text to perform adaptive focus revision on the reference image, thereby enhancing the perception of the modification focus on the composed features. The aforementioned modules collectively constitute the segmentatiOn-based Focus shiFt reviSion nETwork (OFFSET), and comprehensive experiments on four benchmark datasets substantiate the superiority of our proposed method. The codes and data are available on https://zivchen-ty.github.io/OFFSET.github.io/.

Multi-modal recommender systems have become indispensable in modern applications. Despite promising results, existing methods have two key limitations. First, in item characteristic modeling, they rely solely on the merchant’s description and often neglect customer reviews, leading to biased item quality assessment. Second, in user preference modeling, they focus mainly on one-hop user-item interactions in the interaction graph, overlooking multi-hop interactions, which limits the understanding of user preferences. To address these issues, we propose a Joint Content and Interaction Augmented Framework (JCIAF) for multi-modal recommendation. Specifically, we leverage large language models to extract valuable insights from user reviews, integrating this with the merchant’s description to form a more comprehensive textual representation of the item. This enriched description provides a balanced foundation for item characteristic modeling. Next, we enhance the user-item interaction graph with two additional interaction types: user-item-item (two-hop) and user-item-user-item (three-hop), which offer augmented views for more thorough user preference modeling. We apply a diffusion-based method across the three augmented graphs and introduce an online knowledge distillation mechanism to enable cross-graph learning. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method. The source code is accessible at https://github.com/jjlinnn/JClAF.git.

Direct Preference Optimization (DPO) has emerged as an effective approach for mitigating hallucination in Multimodal Large Language Models (MLLMs). Although existing methods have achieved significant progress by utilizing vision-oriented contrastive objectives for enhancing MLLMs' attention to visual inputs and hence reducing hallucination, they suffer from non-rigorous optimization objective function and indirect preference supervision. To address these limitations, we propose a Symmetric Multimodal Preference Optimization (SymMPO), which conducts symmetric preference learning with direct preference supervision (i.e., response pairs) for visual understanding enhancement, while maintaining rigorous theoretical alignment with standard DPO. In addition to conventional ordinal preference learning, SymMPO introduces a preference margin consistency loss to quantitatively regulate the preference gap between symmetric preference pairs. Comprehensive evaluation across five benchmarks demonstrate SymMPO's superior performance, validating its effectiveness in hallucination mitigation of MLLMs.

Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significant potential as universal image retrievers, effectively addressing diverse real-world image retrieval tasks.Nevertheless, pioneering studies, while promising, overlook the potential of fine-grained context modeling and disentangled fine-tuning objectives in enhancing MLLMs' retrieval performance, particularly for complex tasks such as long-text-to-image retrieval, visual dialog retrieval, and composed image retrieval (CIR).Therefore, in this work, we propose an automated fine-grained multimodal quintuple dataset construction pipeline and a novel two-stage fine-grained multimodal fine-tuning strategy.The dataset generation pipeline produces a comprehensive CIR dataset with fine-grained image captions and modification text, facilitating fine-grained context modeling.Beyond the previously entangled fine-tuning paradigm, our approach separates the fine-tuning process into two distinct stages: (1) fine-grained context reasoning-oriented fine-tuning and (2) fine-grained retrieval-oriented fine-tuning.These stages aim to sequentially enhance the model's context understanding and query-target alignment capabilities, thereby improving retrieval performance.Extensive experiments across five datasets encompassing diverse and complex image retrieval tasks demonstrate the remarkable superiority of our method over existing approaches in *Xuemeng Song (sxmustc

Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions. Existing methods mainly consist of a multi-interest extractor that embeds the user interactions into the user multi-interest embeddings, and a multi-interest aggregator that aggregates the learned multi-interest embeddings to the final user embedding, used for predicting the user rating to an item. Despite their effectiveness, existing methods have two key limitations: 1) they directly feed the user interactions into the multi-interest extractor and aggregator, while ignoring their different learning objectives, and 2) they merely consider the centrality of the user interactions to capture the user interests, while overlooking their dispersion. To tackle these limitations, we propose a prompt-based multi-interest learning method (PoMRec), where specific prompts are inserted into the inputted user interactions to make them adaptive to the multi-interest extractor and aggregator. Moreover, we utilize both the mean and variance embeddings of user interactions to embed the user multiple interests for the comprehensively user interest learning. We conduct extensive experiments on three public datasets, and the results verify that our proposed PoMRec outperforms the state-of-the-art multi-interest learning methods.

High Definition (HD) maps, containing detailed road information, are essential for autonomous driving and many geo-related tasks. Recent developments in computer vision make it possible to automate the labor-intensive HD map maintenance work, such as localizing traffic signs within a road network. However, updating traffic signs to HD maps is non-trivial, as it not only requires precise geo-location but also requires confirming whether a sign belongs to a specific road. In our work, we develop an end-to-end automated traffic sign update system, termed AutoTS, which is capable of using an image sequence collected during vehicle operation to extract the geo-location of a traffic sign and determine whether it belongs to the road driven on, from its orientation. In AutoTS, we design a noise and sparsity adaptive localization module, which can filter noisy location points and derive a geo-location from sparse location points. To identify the orientation of traffic signs, we devise a position-aware orientation classification module, which uses the ROI feature and the position-aware SIFT feature to explore the orientation characteristic and understand the road context. To facilitate the evaluation of the proposed method, we construct a traffic sign localization and orientation classification benchmark, KITTI-TS. Our AutoTS achieves an MAE of 2.38 meters in traffic sign localization, while the accuracy in orientation classification reaches 88.89%.

Superpixel segmentation aims to automatically group visually similar pixels within an image into compact regions. This approach provides an efficient low-level representation of image data, effectively reducing the complexity of image primitives for subsequent vision tasks. Recent deep convolutional networks have shown their advantages in superpixel segmentation task. However, many existing deep learning methods still struggle to preserve object edges and accurately perceive similar pixels. This limitation can be attributed to their inadequate ability to model edge information and capture effective context within the image. To address these issues, we propose an Edge guided Local-Global Attention Network (ELGANet) for superpixel segmentation. Specifically, we first devise an Edge Enhancement Module (EeEM), which integrates multiple edge features into the superpixel-friendly features. Then, we develop a Local-Global Attention Module (LGAM) to analyze the relationship between pixels and local or global region patches, expecting to obtain effective context information for grouping similar pixels. The edge features and deep global semantic features are subsequently fused to generate the superpixel-friendly features. The final superpixel-friendly features are then mapped into final superpixels. Extensive experiments on four benchmark datasets demonstrate the effectiveness and superiority of our ELGANet compared with ten state-of-the-art models.

Few-shot food recognition aims to first train a meta-model based on an extensive labeled dataset, and then adapt it to recognize novel food classes with limited labeled data. Although existing studies have achieved compelling success, they still heavily relied on a large number of labeled food data for training the initial meta-model. To save the annotation cost, we propose the unsupervised food recognition task, which aims to train a meta-model using only unlabeled food data. Due to the two challenges presented in food images: 1) high intra-class variations and 2) high inter-class similarity, directly applying existing unsupervised few-shot learning methods could result in sub-optimal results. Towards this end, we propose a novel framework,i.e., Unsupervsied Few-shot Food Recognition with Intra-class Variation and Inter-class Similarity (UFFR-IVIS). It consists of two key components: (1) dual diversity-injected support/query representation learning that introduces instance-level and representation-level diversities for the representation learning of support/query instance to model the characteristics of high intra-class variation; and (2) dual regularization-enhanced meta learning that designs two regularizations: auxiliary task-based intra-class regularization and similarity-guided inter-class regularization to regularize the intra-class variation and inter-class similarity modeling, respectively. Extensive experiments on two food datasets demonstrate the superiority of our UFFR-IVIS.

Sarcasm Explanation in Dialogue (SED) is a new yet challenging task, which aims to generate a natural language explanation for the given sarcastic dialogue that involves multiple modalities (i.e.,utterance, video, and audio). Although existing studies have achieved great success based on the generative pretrained language model BART, they overlook exploiting the sentiments residing in the utterance, video and audio, which play important roles in reflecting sarcasm that essentially involves subtle sentiment contrasts. Nevertheless, it is non-trivial to incorporate sentiments for boosting SED performance, due to three main challenges: 1) diverse effects of utterance tokens on sentiments; 2) gap between video-audio sentiment signals and the embedding space of BART; and 3) various relations among utterances, utterance sentiments, and video-audio sentiments. To tackle these challenges, we propose a novel sEntiment-enhanceD Graph-based multimodal sarcasm Explanation framework, named EDGE. In particular, we first propose a lexicon-guided utterance sentiment inference module, where a heuristic utterance sentiment refinement strategy is devised. We then develop a module named Joint Cross Attention-based Sentiment Inference (JCA-SI) by extending the multimodal sentiment analysis model JCA to derive the joint sentiment label for each video-audio clip. Thereafter, we devise a context-sentiment graph to comprehensively model the semantic relations among the utterances, utterance sentiments, and video-audio sentiments, to facilitate sarcasm explanation generation. Extensive experiments on the publicly released dataset WITS verify the superiority of our model over cutting-edge methods.

In recent years, the use of large-scale pre-trained models for vision-language tasks has gained significant attention and has shown promising results in the video question answering. However, the increasing size of these models has made the fully fine-tuning strategy impractical. Therefore, there is a growing need for research in parameter-efficient transfer learning for downstream tasks. To address this challenge, we introduce a novel parameter-efficient transfer learning technique based on a temporal reasoning adapter for the video question answering task. Our proposed approach captures the temporal relationship within videos, enabling the model to possess visual reasoning ability and knowledge acquisition ability from language models. Our extensive experiments on four video question answering datasets indicate that our method can match or even outperform fully fine-tuning strategies and state-of-the-art models, while having the advantage of parameter efficiency.

Recommending fashion items often leverages rich user profiles and makes targeted suggestions based on past history and previous purchases. In this paper, we work under the assumption that no prior knowledge is given about a user. We propose to build a user profile on the fly by integrating user reactions as we recommend complementary items to compose an outfit. We present a reinforcement learning agent capable of suggesting appropriate garments and ingesting user feedback so to improve its recommendations and maximize user satisfaction. To train such a model, we resort to a proxy model to be able to simulate having user feedback in the training loop. We experiment on the IQON3000 fashion dataset and we find that a reinforcement learning-based agent becomes capable of improving its recommendations by taking into account personal preferences. Furthermore, such task demonstrated to be hard for non-reinforcement models, that cannot exploit exploration during training.

Textual response generation is a pivotal yet challenging task for multimodal task-oriented dialog systems, which targets at generating the appropriate textual response given the multimodal context. Although existing efforts have obtained remarkable advancements, they ignore the potential of the domain information in revealing the key points of the user intention and the user’s history dialogs in indicating the user’s characteristics. To address this issue, in this work, we propose a novel domain-aware multimodal dialog system with distribution-based user characteristic modeling (named DMDU). In particular, DMDU contains three vital components: context-knowledge embedding extraction , domain-aware response generation , and distribution-based user characteristic injection . Specifically, the context-knowledge embedding extraction component aims to extract the embedding of multimodal context and related knowledge following existing studies. The domain-aware response generation component targets at conducting domain-aware fine-grained intention modeling based on the context and knowledge embedding, and thus fulfills the textual response generation. Moreover, the distribution-based user characteristic injection component first captures the user’s characteristics and current intention with the Gaussian distribution and then conducts the sampling-based contrastive semantic regularization to promote the context representation learning. Experimental results on the public dataset demonstrate the effectiveness of DMDU. We release codes to promote other researchers.

Benefiting from instrumental global dependency modeling of self-attention (SA), transformer-based approaches have become the pivotal choices for numerous downstream visual reasoning tasks, such as visual question answering (VQA) and referring expression comprehension (REC). However, some studies have recently suggested that SA tends to suffer from rank collapse thereby inevitably leads to representation degradation as the transformer layer goes deeper. Inspired by social network theory, we attempt to make an analogy between social behavior and regional information interaction in SA, and harness two crucial notions of structural hole and degree centrality in social network to explore the possible optimization towards SA learning, which naturally deduces two plug-and-play social-like modules. Based on structural hole, the former module allows to make information interaction in SA more structured, which effectively avoids redundant information aggregation and global feature homogenization for better rank remedy, followed by latter module to comprehensively characterize and refine the representation discrimination via considering degree centrality of regions and transitivity of relations. Without bells and whistles, our model outperforms a bunch of baselines by a noticeable margin when considering our social-like prior on five benchmarks in VQA and REC tasks, and a series of explanatory results are showcased to sufficiently reveal the social-like behaviors in SA.

Existing sign language translation methods follow a two-stage pipeline: first converting the sign language video to a gloss sequence (i.e., Sign2Gloss) and then translating the generated gloss sequence into a spoken language sentence (i.e., Gloss2Text). While previous studies have focused on boosting the performance of the Sign2Gloss stage, we emphasize the optimization of the Gloss2Text stage. However, this task is non-trivial due to two distinct features of Gloss2Text: (1) isolated gloss input and (2) low-capacity gloss vocabulary. To address these issues, we propose a vision and context knowledge enhanced Gloss2Text model, named VK-G2T, which leverages the visual content of the sign language video to learn the properties of the target sentence and exploit the context knowledge to facilitate the adaptive translation of gloss words. Extensive experiments conducted on a Chinese benchmark validate the superiority of our model.

The challenge of interpreting the world from a human perspective in Artificial Intelligence (AI) is particularly evident in egocentric video question answering, which grapples with issues like small object recognition, noise suppression, and spatial-temporal reasoning. To address these challenges, we introduce the Multi-Factor Adaptive vision Selection (MFAS) framework. MFAS integrates a patch partition and merging module for enhanced small object recognition, a prior-guided patch selection module for noise suppression and focused analysis, and a hierarchical aggregation network to aggregate visual semantics guided by questions. Extensive experiments on several public egocentric datasets have validated the effectiveness and generalization of our framework. Code and data are available in https://github.com/Hyu-Zhang/EgoVideoQA.

Composed Image Retrieval (CIR) allows users to search target images with a multimodal query, comprising a reference image and a modification text that describes the user's modification demand over the reference image. Nevertheless, due to the expensive labor cost of training data annotation, recent researchers have shifted to the challenging task of zero-shot CIR (ZS-CIR), which targets fulfilling CIR without annotated triplets. The pioneer ZS-CIR studies focus on converting the CIR task into a standard text-to-image retrieval task by pre-training a textual inversion network that can map a given image into a single pseudo-word token. Despite their significant progress, their coarse-grained textual inversion may be insufficient to capture the full content of the image accurately. To overcome this issue, in this work, we propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named FTI4CIR. In particular, FTI4CIR comprises two main components: fine-grained pseudo-word token mapping and tri-wise caption-based semantic regularization. The former maps the image into a subject-oriented pseudo-word token and several attribute-oriented pseudo-word tokens to comprehensively express the image in the textual form, while the latter works on jointly aligning the fine-grained pseudo-word tokens to the real-word token embedding space based on a BLIP-generated image caption template. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our proposed method.

Composed image retrieval (CIR) aims to retrieve the target image based on a multimodal query, i.e., a reference image paired with corresponding modification text. Recent CIR studies leverage vision-language pre-trained (VLP) methods as the feature extraction backbone and perform nonlinear feature-level multimodal query fusion to retrieve the target image. Despite the promising performance, we argue that their nonlinear feature-level multimodal fusion may lead to the fused feature deviating from the original embedding space, potentially hurting the retrieval performance. To address this issue, in this work, we propose shifting the multimodal fusion from the feature level to the raw-data level to fully exploit the VLP model's multimodal encoding and cross-modal alignment abilities. In particular, we introduce a Dual Query Unification-based Composed Image Retrieval framework (DQU-CIR), whose backbone simply involves a VLP model's image encoder and a text encoder. Specifically, DQU-CIR first employs two training-free query unification components to derive a unified textual and visual query based on the raw data of the multimodal query, respectively. The unified textual query is derived by concatenating the modification text with the extracted reference image's textual description, while the unified visual query is created by writing the key modification words onto the reference image. Ultimately, to address diverse search intentions, DQU-CIR linearly combines the features of the two unified queries encoded by the VLP model to retrieve the target image. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method.

Query-oriented micro-video summarization task aims to generate a concise sentence with two properties: (a) summarizing the main semantic of the micro-video and (b) being expressed in the form of search queries to facilitate retrieval. Despite its enormous application value in the retrieval area, this direction has barely been explored. Previous studies of summarization mostly focus on the content summarization for traditional long videos. Directly applying these studies is prone to gain unsatisfactory results because of the unique features of micro-videos and queries: diverse entities and complex scenes within a short time, semantic gaps between modalities, and various queries in distinct expressions. To specifically adapt to these characteristics, we propose a query-oriented micro-video summarization model, dubbed QMS. It employs an encoder-decoder-based transformer architecture as the skeleton. The multi-modal (visual and textual) signals are passed through two modal-specific encoders to obtain their representations, followed by an entity-aware representation learning module to identify and highlight critical entity information. As to the optimization, regarding the large semantic gaps between modalities, we assign different confidence scores according to their semantic relevance in the optimization process. Additionally, we develop a novel strategy to sample the effective target query among the diverse query set with various expressions. Extensive experiments demonstrate the superiority of the QMS scheme, on both the summarization and retrieval tasks, over several state-of-the-art methods.

The composed image retrieval (CIR) task aims to retrieve the desired target image for a given multimodal query, i.e., a reference image with its corresponding modification text. The key limitations encountered by existing efforts are two aspects: 1) ignoring the multiple query-target matching factors; 2) ignoring the potential unlabeled reference-target image pairs in existing benchmark datasets. To address these two limitations is non-trivial due to the following challenges: 1) how to effectively model the multiple matching factors in a latent way without direct supervision signals; 2) how to fully utilize the potential unlabeled reference-target image pairs to improve the generalization ability of the CIR model. To address these challenges, in this work, we first propose a CLIP-Transformer based muLtI-factor Matching Network (LIMN), which consists of three key modules: disentanglement-based latent factor tokens mining, dual aggregation-based matching token learning, and dual query-target matching modeling. Thereafter, we design an iterative dual self-training paradigm to further enhance the performance of LIMN by fully utilizing the potential unlabeled reference-target image pairs in a weakly-supervised manner. Specifically, we denote the iterative dual self-training paradigm enhanced LIMN as LIMN+. Extensive experiments on four datasets, including FashionIQ, Shoes, CIRR, and Fashion200 K, show that our proposed LIMN and LIMN+ significantly surpass the state-of-the-art baselines.

Talking head generation, aiming to create photo-realistic videos from a single reference image and audio input, has emerged as a vibrant area of interest within the computer vision community. Despite notable advancements, several challenges remain unaddressed. For instance, many existing approaches overlook the nuanced relationship between audio semantics and head movement, such as nodding in agreement during affirmative expressions. Additionally, the visual quality of generated content, particularly in depicting teeth, often falls short of achieving authentic realism. To address these limitations, we introduce a groundbreaking audio-semantic enhanced pose-driven talking head generation method. Our approach encompasses a multimodal 3DMM parameter prediction network alongside a high-fidelity video synthesis network, meticulously designed to produce authentic and high-quality talking head videos. The multimodal 3DMM parameter prediction network harnesses both acoustic and audio-deduced semantic information, facilitating accurate head pose predictions that resonate with the semantics of spoken words. Furthermore, to significantly improve the depiction of the mouth area, especially the teeth, our video synthesis stage incorporates a mouth-enhanced network augmented by both local and global discriminators. Comprehensive evaluations across diverse metrics affirm the superiority of our method.

Due to its wide applications, multimodal emotion recognition has gained increasing research attention. Although existing methods have achieved compelling success with various multimodal fusion methods, they overlook that the dominated modality (e.g., text) may cause a shortcut and hence negatively affect the representation learning of other modalities (e.g., image and audio). To alleviate such a problem, we resort to the knowledge distillation to narrow the gap between different modalities. In particular, we develop a new hierarchical knowledge distillation model for multi-modal emotion recognition (HKD-MER), consisting of three components, feature extraction, hierarchical knowledge distillation, and attentive multi-modal fusion. As the major contribution in our proposed model, the hierarchical knowledge distillation is designed to transfer the knowledge from the dominant modality to the others at both the feature and label levels. It boosts the performance of non-dominated modalities by modeling the inter-modal relation between different modalities. We have justified the effectiveness of our proposed model over two benchmark datasets.

Recently, fashion compatibility modeling, which can score the matching degree of several complementary fashion items, has gained increasing research attention. Previous studies have primarily learned the features of fashion items and utilize their interaction as the fashion compatibility. However, the try-on looking of an outfit help us to learn the fashion compatibility in a combined manner, where items are spatially distributed and partially covered by other items. Inspired by this, we design a try-on-enhanced fashion compatibility modeling framework, named TryonCM2, which incorporates the try-on appearance with the item interaction to enhance the fashion compatibility modeling. Specifically, we treat each outfit as a sequence of items and adopt the bidirectional long short-term memory (LSTM) network to capture the latent interaction of fashion items. Meanwhile, we synthesize a try-on template image to depict the try-on appearance of an outfit. And then, we regard the outfit as a sequence of multiple image stripes, i.e., local content, of the try-on template, and adopt the bidirectional LSTM network to capture the contextual structure in the try-on appearance. Ultimately, we combine the fashion compatibility lying in the item interaction and try-on appearance as the final compatibility of the outfit. Both the objective and subjective experiments on the existing FOTOS dataset demonstrate the superiority of our framework over the state-of-the-art methods.

Text response generation for multimodal task-oriented dialog systems, which aims to generate the proper text response given the multimodal context, is an essential yet challenging task. Although existing efforts have achieved compelling success, they still suffer from two pivotal limitations: (1) overlook the benefit of generative pretraining and (2) ignore the textual context-related knowledge . To address these limitations, we propose a novel dual knowledge-enhanced generative pretrained language mode for multimodal task-oriented dialog systems (DKMD), consisting of three key components: dual knowledge selection , dual knowledge-enhanced context learning , and knowledge-enhanced response generation . To be specific, the dual knowledge selection component aims to select the related knowledge according to both textual and visual modalities of the given context. Thereafter, the dual knowledge-enhanced context learning component targets seamlessly, integrating the selected knowledge into the multimodal context learning from both global and local perspectives, where the cross-modal semantic relation is also explored. Moreover, the knowledge-enhanced response generation component comprises a revised BART decoder, where an additional dot-product knowledge-decoder attention sub-layer is introduced for explicitly utilizing the knowledge to advance the text response generation. Extensive experiments on a public dataset verify the superiority of the proposed DKMD over state-of-the-art competitors.

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute–value pairs, but overlook that different application scenarios may require texts of different styles. Inspired by this, we define a new task, namely stylized data-to-text generation, whose aim is to generate coherent text for the given non-linguistic data according to a specific style. This task is non-trivial, due to three challenges: the logic of the generated text, unstructured style reference and biased training samples. To address these challenges, we propose a novel stylized data-to-text generation model, named StyleD2T, comprising three components: logic planning-enhanced data embedding, mask-based style embedding, and unbiased stylized text generation. In the first component, we introduce a graph-guided logic planner for attribute organization to ensure the logic of generated text. In the second component, we devise feature-level mask-based style embedding to extract the essential style signal from the given unstructured style reference. In the last one, pseudo triplet augmentation is utilized to achieve unbiased text generation, and a multi-condition based confidence assignment function is designed to ensure the quality of pseudo samples. Extensive experiments on a newly collected dataset from Taobao 1 have been conducted, and the results show the superiority of our model over existing methods.

Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-aligned. The most promising way is to collect image-text pairs from the Internet, but it will inevitably introduce noisy correspondence. To reduce the negative impact of noisy correspondence, we propose a novel model that first transforms the noisy correspondence filtering problem into a similarity distribution modeling problem by exploiting the powerful capabilities of pre-trained models. Specifically, we use the Gaussian Mixture model to model the similarity obtained by CLIP as clean distribution and noisy distribution, to filter out most of the noisy correspondence in the dataset. Afterward, we used relatively clean data to fine-tune the model. To further reduce the negative impact of unfiltered noisy correspondence, i.e., a minimal part where two distributions intersect during the fine-tuning process, we propose a distribution-sensitive dynamic margin ranking loss, further increasing the distance between the two distributions. Through continuous iteration, the noisy correspondence gradually decreases and the model performance gradually improves. Our extensive experiments demonstrate the effectiveness and robustness of our model even under high noise rates.

Video question answering, aiming to answer a natural language question related to the given video, has become prevalent in the past few years. Although remarkable improvements have been obtained, it is still exposed to the challenge of insufficient comprehension of video content. To this end, we propose a spatial-temporal representative visual exploitation network for video question answering, which enhances the understanding of the video by merely summarizing representative visual information. In order to explore representative object information, we advance adaptive attention based on uncertainty estimation. At the same time, to capture representative frame-level and clip-level visual information, we structure a much more compact set of representations iteratively in an expectation-maximization manner to deprecate noisy information. Both the quantitative and qualitative results on NExT-QA, TGIF-QA, MSRVTT-QA, and MSVD-QA datasets demonstrate the superiority of our model over several state-of-the-art approaches.

This article introduces the Universal Relocalizer, a novel approach designed for weakly supervised referring expression grounding. Our method strives to pinpoint a target proposal that corresponds to a specific query, eliminating the need for region-level annotations during training. To bolster the localization precision and enrich the semantic understanding of the target proposal, we devise three key modules: the category module, the color module, and the spatial relationship module. The category and color modules assign respective category and color labels to region proposals, enabling the computation of category and color scores. Simultaneously, the spatial relationship module integrates spatial cues, yielding a spatial score for each proposal to enhance localization accuracy further. By adeptly amalgamating the category, color, and spatial scores, we derive a refined grounding score for every proposal. Comprehensive evaluations on the RefCOCO, RefCOCO+, and RefCOCOg datasets manifest the prowess of the Universal Relocalizer, showcasing its formidable performance across the board.

Multiview spectral clustering, which stands out with its remarkable clustering performance, has drawn increasing research attention. Its core is properly weighing the contribution of different views and comprehensively utilizing multiview information in the clustering process. Although existing methods, like exponential decay and root loss, have achieved significant progress, they still have limitations in their weighting scheme, application generalization, and model efficiency. To handle these limitations, we propose a novel Similarity-Induced Weighted Consensus Laplacian matrix learning method for multiview clustering (MC), named SIWCL. This method has two distinctive features: 1) instead of conventional Laplacian matrix learning, SIWCL resorts to consensus Laplacian matrix learning as the MC framework for effectively exploiting complementary information from multiple views and 2) we argue that there could be outlier views that exhibit an uneven similarity distribution with other views, and equally treating them with other views can hurt model performance. Therefore, beyond consensus Laplacian matrix learning, SIWCL introduces a novel weighting strategy that adaptively assigns weights to views according to their consistency with other views based on the multiview similarity matrices. Notably, this novel method has only one hyperparameter and closed-form solutions, greatly improving the efficiency and generalization. Experiments show that the weights obtained by the proposed weighting strategy are correlated to the quality of the clustering structure. The comparisons between the proposed method and other state-of-the-art baseline methods over eight datasets demonstrate the robustness and superior clustering performance of SIWCL.

Sarcasm is a sophisticated linguistic phenomenon that is prevalent on today's social media platforms. Multi-modal sarcasm detection aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic. This task's key lies in capturing both inter- and intra-modal incongruities within the same context. Although existing methods have achieved compelling success, they are disturbed by irrelevant information extracted from the whole image and text, or overlooking some important information due to the incomplete input. To address these limitations, we propose a Mutual-enhanced Incongruity Learning Network for multi-modal sarcasm detection, named MILNet. In particular, we design a local semantic-guided incongruity learning module and a global incongruity learning module. Moreover, we introduce a mutual enhancement module to take advantage of the underlying consistency between the two modules to boost the performance. Extensive experiments on a widely-used dataset demonstrate the superiority of our model over cutting-edge methods.

Multimodal Sarcasm Explanation (MuSE) is a new yet challenging task, which aims to generate a natural language sentence for a multimodal social post (an image as well as its caption) to explain why it contains sarcasm.Although the existing pioneer study has achieved great success with the BART backbone, it overlooks the gap between the visual feature space and the decoder semantic space, the objectlevel metadata of the image, as well as the potential external knowledge.To solve these limitations, in this work, we propose a novel mulTisource sEmantic grAph-based Multimodal sarcasm explanation scheme, named TEAM.In particular, TEAM extracts the object-level semantic meta-data instead of the traditional global visual features from the input image.Meanwhile, TEAM resorts to ConceptNet to obtain the external related knowledge concepts for the input text and the extracted object metadata.Thereafter, TEAM introduces a multisource semantic graph that comprehensively characterize the multi-source (i.e., caption, object meta-data, external knowledge) semantic relations to facilitate the sarcasm reasoning.Extensive experiments on a public released dataset MORE verify the superiority of our model over cutting-edge methods.

Composed image retrieval (CIR) is a new and flexible image retrieval paradigm, which can retrieve the target image for a multimodal query, including a reference image and its corresponding modification text. Although existing efforts have achieved compelling success, they overlook the conflict relationship modeling between the reference image and the modification text for improving the multimodal query composition and the adaptive matching degree modeling for promoting the ranking of the candidate images that could present different levels of matching degrees with the given query. To address these two limitations, in this work, we propose a Target-Guided Composed Image Retrieval network (TG-CIR). In particular, TG-CIR first extracts the unified global and local attribute features for the reference/target image and the modification text with the contrastive language-image pre-training model (CLIP) as the backbone, where an orthogonal regularization is introduced to promote the independence among the attribute features. Then TG-CIR designs a target-query relationship-guided multimodal query composition module, comprising a target-free student composition branch and a target-based teacher composition branch, where the target-query relationship is injected into the teacher branch for guiding the conflict relationship modeling of the student branch. Last, apart from the conventional batch-based classification loss, TG-CIR additionally introduces a batch-based target similarity-guided matching degree regularization to promote the metric learning process. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed method.

Textual response generation is an essential task for multimodal task-oriented dialog systems. Although existing studies have achieved fruitful progress, they still suffer from two critical limitations: 1) focusing on the attribute knowledge but ignoring the relation knowledge that can reveal the correlations between different entities and hence promote the response generation, and 2)only conducting the cross-entropy loss based output-level supervision but lacking the representation-level regularization. To address these limitations, we devise a novel multimodal task-oriented dialog system (named MDS-S2). Specifically, MDS-S2 first simultaneously acquires the context related attribute and relation knowledge from the knowledge base, whereby the non-intuitive relation knowledge is extracted by the n-hop graph walk. Thereafter, considering that the attribute knowledge and relation knowledge can benefit the responding to different levels of questions, we design a multi-level knowledge composition module in MDS-S^2 to obtain the latent composed response representation. Moreover, we devise a set of latent query variables to distill the semantic information from the composed response representation and the ground truth response representation, respectively, and thus conduct the representation-level semantic regularization. Extensive experiments on a public dataset have verified the superiority of our proposed MDS-S2. We have released the codes and parameters to facilitate the research community.

Multimodal sentence summarization, aiming to generate a brief summary of the source sentence and image, is a new yet challenging task. Although existing methods have achieved compelling success, they still suffer from two key limitations: 1) lacking the adaptation of generative pre-trained language models for open-domain MMSS, and 2) lacking the explicit critical information modeling. To address these limitations, we propose a BART-MMSS framework, where BART is adopted as the backbone. To be specific, we propose a prompt-guided image encoding module to extract the source image feature. It leverages several soft to-be-learned prompts for image patch embedding, which facilitates the visual content injection to BART for open-domain MMSS tasks. Thereafter, we devise an explicit source critical token learning module to directly capture the critical tokens of the source sentence with the reference of the source image, where we incorporate explicit supervision to improve performance. Extensive experiments on a public dataset fully validate the superiority of our proposed method. In addition, the predicted tokens by the vision-guided key-token highlighting module can be easily understood by humans and hence improve the interpretability of our model.

Illegal live-streaming identification, which aims to help live-streaming platforms immediately recognize the illegal behaviors in the live-streaming, such as selling precious and endangered animals, plays a crucial role in purifying the network environment. Traditionally, the live-streaming platform needs to employ some professionals to manually identify the potential illegal live-streaming. Specifically, the professional needs to search for related evidence from a large-scale knowledge database for evaluating whether a given live-streaming clip contains illegal behavior, which is time-consuming and laborious. To address this issue, in this work, we propose a multimodal evidence retrieval system, named OFAR, to facilitate the illegal live-streaming identification. OFAR consists of three modules: Query Encoder, Document Encoder, and MaxSim-based Contrastive Late Intersection. Both query encoder and document encoder are implemented with the advanced OFA encoder, which is pretrained on a large-scale multimodal dataset. In the last module, we introduce contrastive learning on the basis of the MaxiSim-based late intersection, to enhance the model's ability of query-document matching. The proposed framework achieves significant improvement on our industrial dataset TaoLive, demonstrating the advances of our scheme.

Egocentric early action prediction, which aims to recognize the on-going action in the video captured in the first-person view as early as possible before the action is fully executed, is a new yet challenging task due to the limited partial video input. Pioneer studies focused on solving this task with LSTMs as the backbone and simply compiling the observed video segment and unobserved video segment into a single vector, which hence suffer from two key limitations: lack the non-sequential relation modeling with the video snippet sequence and the correlation modeling between the observed and unobserved video segment. To address these two limitations, in this paper, we propose a novel multimodal TransfoRmer-based duAl aCtion prEdiction (mTRACE) model for the task of egocentric early action prediction, which consists of two key modules: the early (observed) segment action prediction module and the future (unobserved) segment action prediction module. Both modules take Transformer encoders as the backbone for encoding all the potential relations among the input video snippets, and involve several single-modal and multi-modal classifiers for comprehensive supervision. Different from previous work, each of the two modules outputs two multi-modal feature vectors: one for encoding the current input video segment, and the other one for predicting the missing video segment. For optimization, we design a two-stage training scheme, including the mutual enhancement stage and end-to-end aggregation stage. The former stage alternatively optimizes the two action prediction modules, where the correlation between the observed and unobserved video segment is modeled with a consistency regularizer, while the latter seamlessly aggregates the two modules to fully utilize the capacity of the two modules. Extensive experiments have demonstrated the superiority of our proposed model. We have released the codes and the corresponding parameters to benefit other researchers athttps://trace729.wixsite.com/trace.

Unsupervised multi-view feature selection aims to select informative features with multi-view features and unsupervised learning. It is a challenging problem due to the absence of explicit semantic supervision. Recently, graph theory and hard pseudo-label learning have been adopted to solve multi-view feature selection problems under the unsupervised learning paradigm. However, graph-based methods are difficult to support large-scale real scenarios due to the high computational complexity of graph construction. Moreover, existing methods based on hard pseudo-label learning generally result in significant information loss. In this article, we propose an Adaptive Collaborative Soft Label Learning (ACSLL) model for unsupervised multi-view feature selection. In this model, collaborative soft label learning and multi-view feature selection are integrated into a unified framework. Specifically, we learn the pseudo soft labels from each view feature by a simple and efficient method and fuse them with an adaptive weighting strategy into a joint soft label matrix. This matrix is further used for guiding the feature selection process to identify valuable features. An effective optimization strategy guaranteed with proven convergence is derived to iteratively solve this problem. Experiments demonstrate the superiority of the proposed method in both feature selection accuracy and efficiency.

Graph embedding, which aims to learn low-dimensional node representations to preserve original graph structures, has attracted extensive research interests. However, most existing graph embedding models represent nodes in Euclidean spaces, which cannot effectively preserve complex patterns, e.g., hierarchical structures. Very recently, several hyperbolic embedding models have been proposed to preserve the hierarchical information in negative curvature spaces. Nevertheless, existing hyperbolic models fail to model the asymmetric proximity between nodes. To address this, we investigate a new asymmetric hyperbolic network representation problem, which targets at jointly preserving the hierarchical structures and asymmetric proximity for general directed graphs. We solve this problem by proposing a novelRotatedLorentzianEmbedding (ROLE) model, which yields two main benefits. First, our model can effectively capture both implicit and explicit hierarchical structures that come from the network topology and category information of nodes, respectively. Second, it can model the asymmetric proximity using rotation transformations. Specifically, we represent each node with a Lorentzian embedding vector, and learn two rotation matrices to reflect the direction of edges. We conduct extensive experiments on four real-world directed graph datasets. Empirical results demonstrate that the proposed approach consistently outperforms various state-of-the-art embedding models. In particular, ROLE achieves HR@1 scores up to 19.8% higher and NDCG@5 scores up to 11.3% higher than the best baselines on the task of node recommendation.

Existing studies on fashion item recommendation mainly focused on incorporating the visual signals of items to boost the user preference learning, while overlooking the semantic attributes (e.g., material and brand) of fashion items that also contain important cues about items’ properties and users’ preference. To bridge this gap, we aim to comprehensively explore the attribute and vision modalities of items to improve the fashion item recommendation performance. However, this is non-trivial due to the latent visual-semantic consistency, various relation types, and unique attributes with insufficient samples. To address these challenges, we propose a Multi-Modal enhanced Fashion item Recommendation scheme (MM-FRec). Specifically, to cope with the multi-modal data, we introduce a relation-oriented graph as well as a vision-oriented graph, and design MM-FRec with three key components: attribute-enhanced latent representation learning, visual representation learning, and multi-modal enhanced preference modeling. To deal with the various relation types, we present a new relation-aware propagation method for adaptively aggregating the information from neighbor nodes to promote the user and item representation learning. To cope with the unique attributes, we introduce the deep multi-task learning strategy in the relation-aware confidence assignment. Extensive experiments on a real-world dataset demonstrate the superiority of our model over state-of-the-art methods.

In the context of COVID-19, numerous people present their opinions through social networks. It is thus highly desired to conduct sentiment analysis towards COVID-19 tweets to learn the public's attitudes, and facilitate the government to make proper guidelines for avoiding the social unrest. Although many efforts have studied the text-based sentiment classification from various domains (e.g., delivery and shopping reviews), it is hard to directly use these classifiers for the sentiment analysis towards COVID-19 tweets due to the domain gap. In fact, developing the sentiment classifier for COVID-19 tweets is mainly challenged by the limited annotated training dataset, as well as the diverse and informal expressions of user-generated posts. To address these challenges, we construct a large-scale COVID-19 dataset from Weibo and propose a dual COnsistency-enhanced semi-superVIseD network for Sentiment Anlaysis (COVID-SA). In particular, we first introduce a knowledge-based augmentation method to augment data and enhance the model's robustness. We then employ BERT as the text encoder backbone for both labeled data, unlabeled data, and augmented data. Moreover, we propose a dual consistency (i.e., label-oriented consistency and instance-oriented consistency) regularization to promote the model performance. Extensive experiments on our self-constructed dataset and three public datasets show the superiority of COVID-SA over state-of-the-art baselines on various applications.

Scene Graph Generation (SGG) is to abstract the objects and their semantic relationships within a given image. Current SGG performance is mainly limited by the biased predicate prediction caused by the long-tailed data distribution. Though many unbiased SGG methods have emerged to enhance the prediction of the tail predicates, their improvements on the tail predicates are often accompanied by the deterioration on the head ones, leading the prediction overly debiased. Toward this end, in this work, we propose a Dual-Biased Predicate Predictor (DBiased-P) to boost the unbiased SGG, which comprises a re-weighted primary classifier and an unweighted auxiliary classifier. The former classifier is tail-biased and used for the final predicate prediction, while the latter one is head-biased and designed to boost the head predicate prediction of the primary classifier by a head-oriented soft regularization. Experiments conducted on Visual Genome and Open Image datasets indicate the superiority of our DBiased-P in unbiased SGG, which significantly improves the recall@50 of the state-of-the-art unbiased SGG method DT2-ACBS from 23.3% to 55.5% as well as the mean recall@50 from 35.9% to 37.7%.

Outfit compatibility modeling, which aims to automatically evaluate the matching degree of an outfit, has drawn great research attention. Regarding the comprehensive evaluation, several previous studies have attempted to solve the task of outfit compatibility modeling by integrating the multi-modal information of fashion items. However, these methods primarily focus on fusing the visual and textual modalities, but seldom consider the category modality as an essential modality. In addition, they mainly focus on the exploration of the intra-modal compatibility relation among fashion items in an outfit but ignore the importance of the inter-modal compatibility relation,i.e.,the compatibility across different modalities between fashion items. Since each modality of the item could deliver the same characteristics of the item as other modalities, as well as certain exclusive features of the item, overlooking the inter-modal compatibility could yield sub-optimal performance. To address these issues, a multi-modal outfit compatibility modeling scheme with modality-oriented graph learning is proposed, dubbed as MOCM-MGL, which takes both the visual, textual, and category modalities as input and jointly propagates the intra-modal and inter-modal compatibilities among fashion items. Experimental results on the real-world Polyvore Outfits-ND and Polyvore Outfits-D datasets have demonstrated the superiority of our proposed model over existing methods.

One of the important factors affecting micro-video recommender systems is to model the multi-modal user preference on the micro-video. Despite the remarkable performance of prior arts, they are still limited by fusing the user preference derived from different modalities in a unified manner, ignoring the users tend to place different emphasis on different modalities. Furthermore, modality-missing is ubiquity and unavoidable in the micro-video recommendation, some modalities information of micro-videos are lacked in many cases, which negatively affects the multi-modal fusion operations. To overcome these disadvantages, we propose a novel framework for the micro-video recommendation, dubbed Dual Graph Neural Network (DualGNN), upon the user-microvideo bipartite and user co-occurrence graphs, which leverages the correlation between users to collaboratively mine the particular fusion pattern for each user. Specifically, we first introduce a single-modal representation learning module, which performs graph operations on the user-microvideo graph in each modality to capture single-modal user preferences on different modalities. And then, we devise a multi-modal representation learning module to explicitly model the user’s attentions over different modalities and inductively learn the multi-modal user preference. Finally, we propose a prediction module to rank the potential micro-videos for users. Extensive experiments on two public datasets demonstrate the significant superiority of our DualGNN over state-of-the-arts methods.

Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the users and items. Existing methods have primarily learned the user’s preferences and item’s features with vectorized embeddings, and modeled the user’s general preferences to items by the interaction of them. In fact, users have their specific preferences to item attributes and different preferences are usually related. Therefore, exploring the fine-grained preferences as well as modeling the relationships among user’s different preferences could improve the recommendation performance. Toward this end, we propose a dual preference distribution learning framework (DUPLE) , which aims to jointly learn a general preference distribution and a specific preference distribution for a given user, where the former corresponds to the user’s general preference to items and the latter refers to the user’s specific preference to item attributes. Notably, the mean vector of each Gaussian distribution can capture the user’s preferences, and the covariance matrix can learn their relationship. Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes. We then can provide the explanation for each recommended item by checking the overlap between its attributes and the user’s preferred attribute profile. Extensive quantitative and qualitative experiments on six public datasets demonstrate the effectiveness and explainability of the DUPLE method.

Egocentric early action prediction aims to recognize actions from the first-person view by only observing a partial video segment, which is challenging due to the limited context information of the partial video. In this article, to tackle the egocentric early action prediction problem, we propose a novel multi-modal adversarial knowledge distillation framework. In particular, our approach involves a teacher network to learn the enhanced representation of the partial video by considering the future unobserved video segment, and a student network to mimic the teacher network to produce the powerful representation of the partial video and based on that predicting the action label. To promote the knowledge distillation between the teacher and the student network, we seamlessly integrate adversarial learning with latent and discriminative knowledge regularizations encouraging the learned representations of the partial video to be more informative and discriminative toward the action prediction. Finally, we devise a multi-modal fusion module toward comprehensively predicting the action label. Extensive experiments on two public egocentric datasets validate the superiority of our method over the state-of-the-art methods. We have released the codes and involved parameters to benefit other researchers. 1

Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph. Existing SGG approaches generally not only neglect the insufficient modality fusion between vision and language, but also fail to provide informative predicates due to the biased relationship predictions, leading SGG far from practical. Towards this end, we first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the intermodal interaction, to serve as the encoder. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder. Particularly, based on the observation that the recognition capability of one classifier is limited towards an extremely unbalanced dataset, we first deploy a group of classifiers that are expert in distinguishing different subsets of classes, and then cooperatively optimize them from two aspects to promote the unbiased SGG. Experiments conducted on VG and GQA datasets demonstrate that, we not only establish a new state-of-the-art in the unbiased metric, but also nearly double the performance compared with two baselines. Our code is available at https://github.com/dongxingning/SHA-GCL-for-SGG.

Personalized outfit recommendation, which aims to recommend the outfits to a given user according to his/her preference, has gained increasing research attention due to its economic value. Nevertheless, the majority of existing methods mainly focus on improving the recommendation effectiveness, while overlooking the recommendation efficiency. Inspired by this, we devise a novel bi-directional heterogeneous graph hashing scheme, called BiHGH, towards efficient personalized outfit recommendation. In particular, this scheme consists of three key components: heterogeneous graph node initialization, bi-directional sequential graph convolution, and hash code learning. We first unify four types of entities (i.e., users, outfits, items, and attributes) and their relations via a heterogeneous four-partite graph. To perform graph learning, we then creatively devise a bi-directional graph convolution algorithm to sequentially transfer knowledge via repeating upwards and downwards convolution, whereby we divide the four-partite graph into three subgraphs and each subgraph only involves two adjacent entity types. We ultimately adopt the bayesian personalized ranking loss for the user preference learning and design the dual similarity preserving regularization to prevent the information loss during hash learning. Extensive experiments on the benchmark dataset demonstrate the superiority of BiHGH.

With the proliferation of online shopping, fashion recommendation, which aims to provide suitable suggestions to support the consumer's purchase in e-commerce platforms, has gained increasing research attention from both academia and industry. Although existing efforts have achieved great progress, they focus on the visual modality, lacking the exploration of other modalities of items, e.g., the textual descriptions and attributes of items. Accordingly, this workshop targets calling for a coordinated effort to promote the multimedia computing towards fashion recommendation. This workshop will showcase the innovative methodologies and ideas on new yet challenging research problems, including (not limited to) fashion recommendation, interactive fashion recommendation, interactive garment retrieval, and outfit compatibility modeling.

Existing studies on multimodal sentiment analysis heavily rely on textual modality and unavoidably induce the spurious correlations between textual words and sentiment labels. This greatly hinders the model generalization ability. To address this problem, we define the task of out-of-distribution (OOD) multimodal sentiment analysis. This task aims to estimate and mitigate the bad effect of textual modality for strong OOD generalization. To this end, we embrace causal inference, which inspects the causal relationships via a causal graph. From the graph, we find that the spurious correlations are attributed to the direct effect of textual modality on the model prediction while the indirect one is more reliable by considering multimodal semantics. Inspired by this, we devise a model-agnostic counterfactual framework for multimodal sentiment analysis, which captures the direct effect of textual modality via an extra text model and estimates the indirect one by a multimodal model. During the inference, we first estimate the direct effect by the counterfactual inference, and then subtract it from the total effect of all modalities to obtain the indirect effect for reliable prediction. Extensive experiments show the superior effectiveness and generalization ability of our proposed framework.

Fashion Compatibility Modeling (FCM) is a new yet challenging task, which aims to automatically access the matching degree among a set of complementary items. Most of existing methods evaluate the fashion compatibility from the common perspective, but overlook the user's personal preference. Inspired by this, a few pioneers study the Personalized Fashion Compatibility Modeling (PFCM). Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. This is, however, non-trivial due to the heterogeneous contents of different entities, embeddings for new users, and various high-order relations. Towards these ends, we present a novel metapath-guided personalized fashion compatibility modeling, dubbed as MG-PFCM. In particular, we creatively build a heterogeneous graph to unify the three types of entities (i.e., users, items, and attributes) and their relations (i.e., user-item interactions, item-item matching relations, and item-attribute association relations). Thereafter, we design a multi-modal content-oriented user embedding module to learn user representations by inheriting the contents of their interacted items. Meanwhile, we define the user-oriented and item-oriented metapaths, and perform the metapath-guided heterogeneous graph learning to enhance the user and item embeddings. In addition, we introduce the contrastive regularization to improve the model performance. We conduct extensive experiments on the real-world benchmark dataset, which verifies the superiority of our proposed scheme over several cutting-edge baselines. As a byproduct, we have released our source codes to benefit other researchers.

Multi-modal Product Summary Generation is a new yet challenging task, which aims to generate a concise and readable summary for a product given its multi-modal content, e.g., its long text description and image. Although existing methods have achieved great success, they still suffer from three key limitations: 1) overlook the benefit of pre-training, 2) lack the representation-level supervision, and 3) ignore the diversity of the seller-generated data. To address these limitations, in this work, we propose a Vision-to-Prompt based multi-modal product summary generation framework, dubbed as V2P, where a Generative Pre-trained Language Model (GPLM) is adopted as the backbone. In particular, to maintain the original text capability of the GPLM and fully utilize the high-level concepts contained in the product image, we design V2P with two key components: vision-based prominent attribute prediction, and attribute prompt-guided summary generation. The first component works on obtaining the vital semantic attributes of the product from its image by the Swin Transformer, while the second component aims to generate the summary based on the product's long text description and the attribute prompts yielded by the first component with a GPLM. Towards comprehensive supervision over the second component, apart from the conventional output-level supervision, we introduce the representation-level regularization. Meanwhile, we design the data augmentation-based robustness regularization to handle the diverse inputs and improve the robustness of the second component. Extensive experiments on a large-scale Chinese dataset verify the superiority of our model over cutting-edge methods.

Scene Graph Generation (SGG) aims to detect the objects and their pairwise predicates in an image. Existing SGG methods mainly fulfil the challenging predicate prediction task that involves severe long-tailed data distribution with a single classifier. However, we argue that this may be enough to differentiate predicates that present obvious differences (e.g.,$on$and$near$), but not sufficient to distinguish similar predicates that only have subtle differences (e.g.,$on$and$standing~on$). Towards this end, we divide the predicate prediction into a few sub-tasks with a Divide-and-Conquer Predictor (DC-Predictor). Specifically, we first develop an offline pattern-predicate correlation mining algorithm to discover the similar predicates that share the same object interaction pattern. Based on that, we devise a general pattern classifier and a set of specific predicate classifiers for DC-Predictor. The former works on recognizing the pattern of a given object pair and routing it to the corresponding specific predicate classifier, while the latter aims to differentiate similar predicates in each specific pattern. In addition, we introduce the Bayesian Personalized Ranking loss in each specific predicate classifier to enhance the pairwise differentiation between head predicates and their similar ones. Experiments on VG150 and GQA datasets show the superiority of our model over state-of-the-art methods.

Fashion Compatibility Modeling (FCM), which aims to automatically evaluate whether a given set of fashion items makes a compatible outfit, has attracted increasing research attention. Recent studies have demonstrated the benefits of conducting the item representation disentanglement towards FCM. Although these efforts have achieved prominent progress, they still perform unsatisfactorily, as they mainly investigate the visual content of fashion items, while overlooking the semantic attributes of items (e.g., color and pattern), which could largely boost the model performance and interpretability. To address this issue, we propose to comprehensively explore the visual content and attributes of fashion items towards FCM. This problem is non-trivial considering the following challenges: a) how to utilize the irregular attribute labels of items to partially supervise the attribute-level representation learning of fashion items; b) how to ensure the intact disentanglement of attribute-level representations; and c) how to effectively sew the multiple granulairites (i.e, coarse-grained item-level and fine-grained attribute-level) information to enable performance improvement and interpretability. To address these challenges, in this work, we present a partially supervised outfit compatibility modeling scheme (PS-OCM). In particular, we first devise a partially supervised attribute-level embedding learning component to disentangle the fine-grained attribute embeddings from the entire visual feature of each item. We then introduce a disentangled completeness regularizer to prevent the information loss during disentanglement. Thereafter, we design a hierarchical graph convolutional network, which seamlessly integrates the attribute- and item-level compatibility modeling, and enables the explainable compatibility reasoning. Extensive experiments on the real-world dataset demonstrate that our PS-OCM significantly outperforms the state-of-the-art baselines. We have released our source codes and well-trained models to benefit other researchers (https://site2750.wixsite.com/ps-ocm).

Visual attention in Visual Question Answering (VQA) targets at locating the right image regions regarding the answer prediction, offering a powerful technique to promote multi-modal understanding. However, recent studies have pointed out that the highlighted image regions from the visual attention are often irrelevant to the given question and answer, leading to model confusion for correct visual reasoning. To tackle this problem, existing methods mostly resort to aligning the visual attention weights with human attentions. Nevertheless, gathering such human data is laborious and expensive, making it burdensome to adapt well-developed models across datasets. To address this issue, in this article, we devise a novel visual attention regularization approach, namely, AttReg, for better visual grounding in VQA. Specifically, AttReg first identifies the image regions that are essential for question answering yet unexpectedly ignored (i.e., assigned with low attention weights) by the backbone model. And then a mask-guided learning scheme is leveraged to regularize the visual attention to focus more on these ignored key regions. The proposed method is very flexible and model-agnostic, which can be integrated into most visual attention-based VQA models and require no human attention supervision. Extensive experiments over three benchmark datasets, i.e., VQA-CP v2, VQA-CP v1, and VQA v2, have been conducted to evaluate the effectiveness of AttReg. As a by-product, when incorporating AttReg into the strong baseline LMH, our approach can achieve a new state-of-the-art accuracy of 60.00% with an absolute performance gain of 7.01% on the VQA-CP v2 benchmark dataset. In addition to the effectiveness validation, we recognize that the faithfulness of the visual attention in VQA has not been well explored in literature. In the light of this, we propose to empirically validate such property of visual attention and compare it with the prevalent gradient-based approaches.

Natural language generation (NLG) has been an essential technique for various applications, like XiaoIce and Siri, and engaged increasing attention recently. To improve the user experience, several emotion-aware NLG methods have been developed to generate responses coherent with a pre-designated emotion (e.g., the positive or negative). Nevertheless, existing methods cannot generate personalized responses as they frequently overlook the personalized linguistic style. Apparently, different human responsers tend to have different linguistic styles. Inspired by this, in this work, we focus on a novel research theme of personalized emotion-aware NLG ( PENLG ), whereby the generated responses should be coherent with the linguistic style of a pre-designated responser and emotion. In particular, we study PENLG under a scenario of generating personalized emotion-aware response for social media post. Yet it faces certain research challenges: (1) the user linguistic styles are implicit and complex by nature, and hence it is hard to learn their representations; and (2) linguistic styles and emotions are usually expressed in different manners in a response, and thus how to convey them properly in the generated responses is not easy. Toward this end, we present a novel scheme of PENLG, named CRobot, which consists of a personalized emotion-aware response generator and two discriminators, i.e., general discriminator and personalized emotion-aware discriminator . To be more specific, the post-based and avatar-based user linguistic style modeling methods are incorporated into the encoder-decoder–based generator, while the discriminators are devised to ensure that the generated response is fluent and consistent with both the emotion and the linguistic style of the user. Different from the traditional adversarial networks, we embed adversarial learning under the umbrella of reinforcement learning. In this way, the response generation problem can be tackled by the generator taking a sequence of actions on selecting the proper word of each timestep for output. To justify our model, we construct a large-scale response generation dataset based on Twitter, consisting of 6,763 tweets with a corresponding 1,461,713 response created by 153,664 users. Extensive experiments demonstrate that CRobot surpasses the state-of-the-art baselines regarding both subjective and objective evaluation.

Existing methods towards outfit compatibility modeling seldom explicitly consider multimodal correlations. In this work, we explore the consistent and complementary correlations for better compatibility modeling. This is, however, non-trivial due to the following challenges: 1) how to separate and model these two kinds of correlations; 2) how to leverage the derived complementary cues to strengthen the text and vision-oriented representations of the given item; and 3) how to reinforce the compatibility modeling with text and vision-oriented representations. To address these challenges, we present a comprehensive multimodal outfit compatibility modeling scheme. It first nonlinearly projects each modality into separable consistent and complementary spaces via multi-layer perceptron, and then models the consistent and complementary correlations between two modalities by parallel and orthogonal regularization. Thereafter, we strengthen the visual and textual representation of items with complementary information, and further induct both the text-oriented and vision- oriented outfit compatibility modeling. We ultimately employ the mutual learning strategy to reinforce the final performance of compatibility modeling. Extensive experiments demonstrate the superiority of our scheme.

Recently, outfit compatibility modeling, which aims to evaluate the compatibility of a given outfit that comprises a set of fashion items, has gained growing research attention. Although existing studies have achieved prominent progress, most of them overlook the essential global outfit representation learning, and the hidden complementary factors behind the outfit compatibility uncovering. Towards this end, we propose an Outfit Compatibility Modeling scheme via Complementary Factorization, termed as OCM-CF. In particular, OCM-CF consists of two key components: context-aware outfit representation modeling and hidden complementary factors modeling. The former works on adaptively learning the global outfit representation with graph convolutional networks and the multi-head attention mechanism, where the item context is fully explored. The latter targets at uncovering the latent complementary factors with multiple parallel networks, each of which corresponds to a factor-oriented context-aware outfit representation modeling. In this part, a new orthogonality-based complementarity regularization is proposed to encourage the learned factors to complement each other and better characterize the outfit compatibility. Finally, the outfit compatibility is obtained by summing all the hidden complementary factor-oriented outfit compatibility scores, each of which is derived from the corresponding outfit representation. Extensive experiments on two real-world datasets demonstrate the superiority of our OCM-CF over the state-of-the-art methods.

Whether an outfit is compatible? Using machine learning methods to assess an outfit's compatibility, namely, fashion compatibility modeling (FCM), has recently become a popular yet challenging topic. However, current FCM studies still perform far from satisfactory, because they only consider the collocation compatibility modeling, while neglecting the natural human habits that people generally evaluate outfit compatibility from both the collocation (discrete assess) and the try-on (unified assess) perspectives. In light of the above analysis, we propose a Collocation and Try-On Network (CTO-Net) for FCM, combining both the collocation and try-on compatibilities. In particular, for the collocation perspective, we devise a disentangled graph learning scheme, where the collocation compatibility is disentangled into multiple fine-grained compatibilities between items; regarding the try-on perspective, we propose an integrated distillation learning scheme to unify all item information in the whole outfit to evaluate the compatibility based on the latent try-on representation. To further enhance the collocation and try-on compatibilities, we exploit the mutual learning strategy to obtain a more comprehensive judgment. Extensive experiments on the real-world dataset demonstrate that our CTO-Net significantly outperforms the state-of-the-art methods. In particular, compared with the competitive counterparts, our proposed CTO-Net significantly improves AUC accuracy from 83.2% to 87.8% and MRR from 15.4% to 21.8%. We have released our source codes and trained models to benefit other researchers.1

In this paper, we are interested in understanding how customers perceive fashion recommendations, in particular when observing a proposed combination of garments to compose an outfit. Automatically understanding how a suggested item is perceived, without any kind of active engagement, is in fact an essential block to achieve interactive applications. We propose a pixel-landmark mutual enhanced framework for implicit preference estimation, named PLM-IPE, which is capable of inferring the user’s implicit preferences exploiting visual cues, without any active or conscious engagement. PLM-IPE consists of three key modules: pixel-based estimator, landmark-based estimator and mutual learning based optimization. The former two modules work on capturing the implicit reaction of the user from the pixel level and landmark level, respectively. The last module serves to transfer knowledge between the two parallel estimators. Towards evaluation, we collected a real-world dataset, named SentiGarment, which contains 3,345 facial reaction videos paired with suggested outfits and human labeled reaction scores. Extensive experiments show the superiority of our model over state-of-the-art approaches.

Temporal Moment Localization (TML) in untrimmed videos is a challenging task in the field of multimedia, which aims at localizing the start and end points of the activity in the video, described by a sentence query. Existing methods mainly focus on mining the correlation between video and sentence representations or investigating the fusion manner of the two modalities. These works mainly understand the video and sentence coarsely, ignoring the fact that a sentence can be understood from various semantics, and the dominant words affecting the moment localization in the semantics are the action and object reference. Toward this end, we propose a Hierarchical Deep Residual Reasoning (HDRR) model, which decomposes the video and sentence into multi-level representations with different semantics to achieve a finer-grained localization. Furthermore, considering that videos with different resolution and sentences with different length have different difficulty in understanding, we design the simple yet effective Res-BiGRUs for feature fusion, which is able to grasp the useful information in a self-adapting manner. Extensive experiments conducted on Charades-STA and ActivityNet-Captions datasets demonstrate the superiority of our HDRR model compared with other state-of-the-art methods.

In this work, we investigate the user identity linkage task across different social media platforms based on heterogeneous multi-modal posts and social connections. This task is non-trivial due to the following two challenges. 1) As each user involves both intra multi-modal posts and inter social connections, how to accurately fulfil the user representation learning from both intra and inter perspectives constitutes the main challenge. And 2) even representations distributed on different platforms of the same identity tend to be distinct (i.e., the semantic gap problem) owing to discrepant data distribution of different platforms. Hence, how to alleviate the semantic gap problem poses another tough challenge. To this end, we propose a novel adversarial-enhanced hybrid graph network (AHG-Net), consisting of three key components: user representation extraction, hybrid user representation learning, and adversarial learning. Specifically, AHG-Net first employs advanced deep learning techniques to extract the user's intermediate representations from his/her heterogeneous multi-modal posts and social connections. Then AHG-Net unifies the intra-user representation learning and inter-user representation learning with a hybrid graph network. Finally, AHG-Net adopts adversarial learning to encourage the learned user presentations of the same identity to be similar using a semantic discriminator. Towards evaluation, we create a multi-modal user identity linkage dataset by augmenting an existing dataset with 62,021 images collected from Twitter and Foursquare. Extensive experiments validate the superiority of the proposed network. Meanwhile, we release the dataset, codes, and parameters to facilitate the research community.

When talking to the dialog robots, users have to activate the robot first from the standby mode with special wake words, such as "Hey Siri", which is apparently not user-friendly. The latest generation of dialog robots have been equipped with advanced sensors, like the camera, enabling multimodal activation. In this work, we work towards awaking the robot without wake words. To accomplish this task, we present a Multimodal Activation Scheme (MAS), consisting of two key components: audio-visual consistency detection and semantic talking intention inference. The first one is devised to measure the consistency between the audio and visual modalities in order to figure out weather the heard speech comes from the detected user in front of the camera. Towards this end, two heterogeneous CNN-based networks are introduced to convolutionalize the fine-grained facial landmark features and the MFCC audio features, respectively. The second one is to infer the semantic talking intention of the recorded speech, where the transcript of the speech is recognized and matrix factorization is utilized to uncover the latent human-robot talking topics. We ultimately devise different fusion strategies to unify these two components. To evaluate MAS, we construct a dataset containing 12,741 short videos recorded by 194 invited volunteers. Extensive experiments demonstrate the effectiveness of our scheme.

Composing text and image for image retrieval (CTI-IR) is a new yet challenging task, for which the input query is not the conventional image or text but a composition, i.e., a reference image and its corresponding modification text. The key of CTI-IR lies in how to properly compose the multi-modal query to retrieve the target image. In a sense, pioneer studies mainly focus on composing the text with either the local visual descriptor or global feature of the reference image. However, they overlook the fact that the text modifications are indeed diverse, ranging from the concrete attribute changes, like "change it to long sleeves", to the abstract visual property adjustments, e.g., "change the style to professional". Thus, simply emphasizing the local or global feature of the reference image for the query composition is insufficient. In light of the above analysis, we propose a Comprehensive Linguistic-Visual Composition Network (CLVC-Net) for image retrieval. The core of CLVC-Net is that it designs two composition modules: fine-grained local-wise composition module and fine-grained global-wise composition module, targeting comprehensive multi-modal compositions. Additionally, a mutual enhancement module is designed to promote local-wise and global-wise composition processes by forcing them to share knowledge with each other. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our CLVC-Net. We released the codes to benefit other researchers.

GWI survey1has highlighted the flourishing use of multiple social networks: the average number of social media accounts per Internet user is 5.54, and among them, 2.82 are being used actively. Indeed, users tend to express their views in more than one social media site. Hence, merging social signals of the same user across different social networks together, if available, can facilitate the downstream analyses. Previous work has paid little attention on modeling the cooperation among the following factors when fusing data from multiple social networks: 1) as data from different sources characterizes the characteristics of the same social user, the source consistency merits our attention; 2) due to their different functional emphases, some aspects of the same user captured by different social networks can be just complementary and results in the source complementarity; and 3) different sources can contribute differently to the user characterization and hence lead to the different source confidence. Toward this end, we propose a novel unified model, which co-regularizes source consistency, complementarity, and confidence to boost the learning performance with multiple social networks. In addition, we derived its theoretical solution and verified the model with the real-world application of user interest inference. Extensive experiments over several state-of-the-art competitors have justified the superiority of our model.1http://tinyurl.com/zk6kgc9

This paper focuses on tackling the problem of temporal language localization in videos, which aims to identify the start and end points of a moment described by a natural language sentence in an untrimmed video. However, it is non-trivial since it requires not only the comprehensive understanding of the video and sentence query, but also the accurate semantic correspondence capture between them. Existing efforts are mainly centered on exploring the sequential relation among video clips and query words to reason the video and sentence query, neglecting the other intra-modal relations (e.g., semantic similarity among video clips and syntactic dependency among the query words). Towards this end, in this work, we propose a Multi-modal Interaction Graph Convolutional Network (MIGCN), which jointly explores the complex intra-modal relations and inter-modal interactions residing in the video and sentence query to facilitate the understanding and semantic correspondence capture of the video and sentence query. In addition, we devise an adaptive context-aware localization method, where the context information is taken into the candidate moments and the multi-scale fully connected layers are designed to rank and adjust the boundary of the generated coarse candidate moments with different lengths. Extensive experiments on Charades-STA and ActivityNet datasets demonstrate the promising performance and superior efficiency of our model.

In this paper, we work towards linking users’ identities on different social media platforms by exploring the user-generated contents (UGCs). This task is non-trivial due to the following challenges. 1) As UGCs involve multiple modalities (e.g., text and image), how to accurately characterize the user account based on their heterogeneous multi-modal UGCs poses the main challenge. 2) As people tend to post similar UGCs on different social media platforms during the same period, how to effectively model the temporal post correlation is a crucial challenge. And 3) no public benchmark dataset is available to support our user identity linkage based on heterogeneous UGCs with timestamps. Towards this end, we present an attentive time-aware user identity linkage scheme, which seamlessly integrates the temporal post correlation modeling and attentive user similarity modeling. To facilitate the evaluation, we create a comprehensive large-scale user identity linkage dataset from two popular social media platforms: Instagram and Twitter. Extensive experiments have been conducted on our dataset and the results verify the effectiveness of the proposed scheme. As a residual product, we have released the dataset, codes, and parameters to facilitate other researchers.

With the rising incidence of some diseases, such as obesity and diabetes, the healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user-preference-based food recommendation, cooking assistance, or the nutrition and calorie estimation of dishes, ignoring the personalized health-aware food recommendation. Therefore, in this work, we present a personalized health-aware food recommendation scheme, namely, Market2Dish, mapping the ingredients displayed in the market to the healthy dishes eaten at home. The proposed scheme comprises three components, namely, recipe retrieval, user health profiling, and health-aware food recommendation. In particular, recipe retrieval aims to acquire the ingredients available to the users and then retrieve recipe candidates from a large-scale recipe dataset. User health profiling is to characterize the health conditions of users by capturing the textual health-related information crawled from social networks. Specifically, to solve the issue that the health-related information is extremely sparse, we incorporate a word-class interaction mechanism into the proposed deep model to learn the fine-grained correlations between the textual tweets and pre-defined health concepts. For the health-aware food recommendation, we present a novel category-aware hierarchical memory network–based recommender to learn the health-aware user-recipe interactions for better food recommendation. Moreover, extensive experiments demonstrate the effectiveness of the health-aware food recommendation scheme.

With the boom of the fashion market and people’s daily needs for beauty, clothing matching has gained increased research attention. In a sense, tackling this problem lies in modeling the human notions of the compatibility between fashion items, i.e., Fashion Compatibility Modeling (FCM), which plays an important role in a wide bunch of commercial applications, including clothing recommendation and dressing assistant. Recent advances in multimedia processing have shown remarkable effectiveness in accurate compatibility evaluation. However, these studies work like a black box and cannot provide appropriate explanations, which are indeed of importance for gaining users’ trust and improving their experience. In fact, fashion experts usually explain the compatibility evaluation through the matching patterns between fashion attributes (e.g., a silk tank top cannot go with a knit dress). Inspired by this, we devise an attribute-wise explainable FCM solution, named ExFCM , which can simultaneously generate the item-level compatibility evaluation for input fashion items and the attribute-level explanations for the evaluation result. In particular, ExFCM consists of two key components: attribute-wise representation learning and attribute interaction modeling. The former works on learning the region-aware attribute representation for each item with the threshold global average pooling. Besides, the latter is responsible for compiling the attribute-level matching signals into the overall compatibility evaluation adaptively with the attentive interaction mechanism. Note that ExFCM is trained without any attribute-level compatibility annotations, which facilitates its practical applications. Extensive experiments on two real-world datasets validate that ExFCM can generate more accurate compatibility evaluations than the existing methods, together with reasonable explanations.

In recent years, there has been a growing interest in the fashion analysis (e.g., clothing matching) due to the huge economic value of the fashion industry. The essential problem is to model the compatibility between the complementary fashion items, such as the top and bottom in clothing matching. The majority of existing work on fashion analysis has focused on measuring the item-item compatibility in a latent space with deep learning methods. In this work, we aim to improve the compatibility modeling by sketching a compatible template for a given item as an auxiliary link between fashion items. Specifically, we propose an end-to-end Auxiliary Template-enhanced Generative Compatibility Modeling (AT-GCM) scheme, which introduces an auxiliary complementary template generation network equipped with the pixel-wise consistency and compatible template regularization. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed approach.

Recent years have witnessed a growing trend of fashion compatibility modeling, which scores the matching degree of the given outfit and then provides people with some dressing advice. Existing methods have primarily solved this problem by analyzing the discrete interaction among multiple complementary items. However, the fashion items would present certain occlusion and deformation when they are worn on the body. Therefore, the discrete item interaction cannot capture the fashion compatibility in a combined manner due to the neglect of a crucial factor: the overall try-on appearance. In light of this, we propose a multi-modal try-on-guided compatibility modeling scheme to jointly characterize the discrete interaction and try-on appearance of the outfit. In particular, we first propose a multi-modal try-on template generator to automatically generate a try-on template from the visual and textual information of the outfit, depicting the overall look of its composing fashion items. Then, we introduce a new compatibility modeling scheme which integrates the outfit try-on appearance into the traditional discrete item interaction modeling. To fulfill the proposal, we construct a large-scale real-world dataset from SSENSE, named FOTOS, consisting of 11,000 well-matched outfits and their corresponding realistic try-on images. Extensive experiments have demonstrated its superiority to state-of-the-arts.

In this work, we aim to investigate the practical task of flexible fashion search with attribute manipulation, where users can retrieve the target fashion items by replacing the unwanted attributes of an available query image with the desired ones (e.g., changing the collar attribute from v-neck to round). Although several pioneer efforts have been dedicated to fulfilling the task, they mainly ignore the potential of generative models in enhancing the visual understanding of target fashion items. To this end, we propose an end-to-end generative attribute manipulation scheme, which consists of a generator and a discriminator. The generator works on producing the prototype image that meets the user's requirement of attribute manipulation over the query image with the regularization of visual-semantic consistency and pixel-wise consistency. Besides, the discriminator aims to jointly fulfill the semantic learning towards correct attribute manipulation and adversarial metric learning for fashion search. Pertaining to the adversarial metric learning, we provide two general paradigms: the pair-based scheme and the triplet-based scheme, where the fake generated prototype images that closely resemble the ground truth images of target items are incorporated as hard negative samples to boost the model performance. Extensive experiments on two real-world datasets verify the effectiveness of our scheme.

In modern society, clothing matching plays a pivotal role in people's daily life, as suitable outfits can beautify their appearance directly. Nevertheless, how to make a suitable outfit has become a daily headache for many people, especially those who do not have much sense of aesthetics. In the light of this, many research efforts have been dedicated to the task of complementary clothing matching and have achieved great success relying on the advanced data-driven neural networks. However, most existing methods overlook the rich valuable knowledge accumulated by our human beings in the fashion domain, especially the rules regarding clothing matching, like "coats go with dresses" and "silk tops cannot go with chiffon bottoms". Towards this end, in this work, we propose a knowledge-guided neural compatibility modeling scheme, which is able to incorporate the rich fashion domain knowledge to enhance the performance of the compatibility modeling in the context of clothing matching. To better integrate the huge and implicit fashion domain knowledge into the data-driven neural networks, we present a probabilistic knowledge distillation (PKD) method, which is able to encode vast knowledge rules in a probabilistic manner. Extensive experiments on two real-world datasets have verified the guidance of rules from different sources and demonstrated the effectiveness and portability of our model. As a byproduct, we released the codes and involved parameters to benefit the research community.

Due to the complex and dynamic environment of social media, user generated contents (UGCs) may inadvertently leak users’ personal aspects, such as the personal attributes, relationships and even the health condition, and thus place users at high privacy risks. Limited research efforts, thus far, have been dedicated to the privacy detection from users’ unstructured data (i.e., UGCs). Moreover, existing efforts mainly focus on applying conventional machine learning techniques directly to traditional hand-crafted privacy-oriented features, ignoring the powerful representing capability of the advanced neural networks. In light of this, in this article, we present a fine-grained privacy detection network (GrHA) equipped with graph-regularized hierarchical attentive representation learning. In particular, the proposed GrHA explores the semantic correlations among personal aspects with graph convolutional networks to enhance the regularization for the UGC representation learning, and, hence, fulfil effective fine-grained privacy detection. Extensive experiments on a real-world dataset demonstrate the superiority of the proposed model over state-of-the-art competitors in terms of eight standard metrics. As a byproduct, we have released the codes and involved parameters to facilitate the research community.

Recent years have witnessed a flourishing of community-driven question answering (cQA), like Yahoo! Answers and AnswerBag, where people can seek precise information. After 2010, some novel cQA systems, including Quora and Zhihu, gained momentum. Besides interactions, the latter enables users to label the questions with topic tags that highlight the key points conveyed in the questions. In this article, we shed light on automatically annotating a newly posted question with topic tags that are predefined and preorganized into a directed acyclic graph. To accomplish this task, we present an end-to-end deep interactive embedding model to jointly learn the embeddings of questions and topics by projecting them into the same space for a similarity measure. In particular, we first learn the embeddings of questions and topic tags by two deep parallel models. Thereinto, we regularize the embeddings of topic tags via fully exploring their hierarchical structures, which is able to alleviate the problem of imbalanced topic distribution. Thereafter, we interact each question embedding with the topic tag matrix, i.e., all the topic tag embeddings. Following that, a sigmoid cross-entropy loss is appended to reward the positive question-topic pairs and penalize the negative ones. To justify our model, we have conducted extensive experiments on an unprecedented large-scale social QA dataset obtained from Zhihu.com, and the experimental results demonstrate that our model achieves superior performance to several state-of-the-art baselines.

In modern society, people tend to prefer fashionable and decent outfits that can meet more than basic physiological needs. In fact, a proper outfit usually relies on good matching among complementary fashion items (e.g., the top, bottom, and shoes) that compose it, which thus propels us to investigate the automatic complementary clothing matching scheme. However, this is non-trivial due to the following challenges. First, the main challenge lies in how to accurately model the compatibility between complementary fashion items (e.g., the top and bottom) that come from the heterogeneous spaces with multi-modalities (e.g., the visual modality and textual modality). Second, since different features (e.g., the color, style, and pattern) of fashion items may contribute differently to compatibility modeling, how to encode the confidence of different pairwise features presents a tough challenge. Third, how to jointly learn the latent representation of multi-modal data and the compatibility between complementary fashion items contributes to the last challenge. Toward this end, in this work, we present an end-to-end attention-based neural framework for the compatibility modeling, where we introduce a feature-level attention model to adaptively learn the confidence for different pairwise features. Extensive experiments on a public available real-world dataset show the superiority of our model over state-of-the-art methods.

Recent years have witnessed a growing trend of building the capsule wardrobe by minimizing and diversifying the garments in their messy wardrobes. Thanks to the recent advances in multimedia techniques, many researches have promoted the automatic creation of capsule wardrobes by the garment modeling. Nevertheless, most capsule wardrobes generated by existing methods fail to consider the user profile, including the user preferences, body shapes and consumption habits, which indeed largely affects the wardrobe creation. To this end, we introduce a combinatorial optimization-based personalized capsule wardrobe creation framework, named PCW-DC, which jointly integrates both garment modeling (\textiti.e., wardrobe compatibility) and user modeling (\textiti.e., preferences, body shapes). To justify our model, we construct a dataset, named bodyFashion, which consists of $116,532$ user-item purchase records on Amazon involving 11,784 users and 75,695 fashion items. Extensive experiments on bodyFashion have demonstrated the effectiveness of our proposed model. As a byproduct, we have released the codes and the data to facilitate the research community.

What made you want to wear the clothes you are wearing? Where is the place you want to visit for your next-coming holiday? Why do you like the music you frequently listen to? If you are like most people, you probably made these decisions as a result of watching influencers on social media. Furthermore, influencer marketing is an opportunity for brands to take advantage of social media using a well-defined and well-designed social media marketing strategy. However, choosing the right influencers is not an easy task. With more people gaining an increasing number of followers in social media, finding the right influencer for an E-commerce company becomes paramount. In fact, most marketers cite it as a top challenge for their brands. To address the aforementioned issues, we proposed a data-driven micro-influencer ranking scheme to solve the essential question of finding out the right micro-influencer. Specifically, we represented brands and influencers by fusing their historical posts' visual and textual information. A novel k-buckets sampling strategy with a modified listwise learning to rank model were proposed to learn a brand-micro-influncer scoring function. In addition, we developed a new Instagram brand micro-influencer dataset, consisting of 360 brands and 3,748 micro-influencers, which can benefit future researchers in this area. The extensive evaluations demonstrate the advantage of our proposed method compared with the state-of-the-art methods.

Owing to the recent advances in the multimedia processing domain and the publicly available large-scale real-world data provided by online fashion communities, like the IQON and Chictopia, researchers are enabled to investigate the automatic clothing matching solutions. In a sense, existing methods mainly focus on modeling the general item-item compatibility from the aesthetic perspective, but fail to incorporate the user factor. In fact, aesthetics can be highly subjective, as different people may hold different clothing preferences. In light of this, in this work, we attempt to tackle the problem of personalized compatibility modeling from not only the general aesthetics but also the personal preference perspectives. In particular, we present a personalized compatibility modeling scheme GP-BPR, comprising of two essential components: general compatibility modeling and personal preference modeling, which characterize the item-item and user-item interactions, respectively. In particular, due to the concern that both the modalities (e.g., the image and context description) of fashion items can deliver important cues regarding user personal preference, we present a comprehensive personal preference modeling method. Moreover, for evaluation, we create a large-scale dataset, IQON3000, from the online fashion community IQON. Extensive experiment results on IQON3000 verify the effectiveness of the proposed scheme. As a byproduct, we have released the dataset, codes, and involved parameters to benefit other researchers.

Thanks to the recent advance in the multimedia techniques, increasing research attention has been paid to the virtual try-on task, especially with the 2D image modeling. The traditional try-on task aims to align the target clothing item naturally to the given person's body and hence present a try-on look of the person. However, in practice, people may also be interested in their try-on looks with different poses. Therefore, in this work, we introduce a new try-on setting, which enables the changes of both the clothing item and the person's pose. Towards this end, we propose a pose-guided virtual try-on scheme based on the generative adversarial networks (GANs) with a bi-stage strategy. In particular, in the first stage, we propose a shape enhanced clothing deformation model for deforming the clothing item, where the user body shape is incorporated as the intermediate guidance. For the second stage, we present an attentive bidirectional GAN, which jointly models the attentive clothing-person alignment and bidirectional generation consistency. For evaluation, we create a large-scale dataset, FashionTryOn, comprising $28,714$ triplets with each consisting of a clothing item image and two model images in different poses. Extensive experiments on FashionTryOn validate the superiority of our model over the state-of-the-art methods.

Presentation is one of the most important and vivid methods to deliver information to audience. Apart from the content of presentation, how the speaker behaves during presentation makes a big difference. In other words, gestures, as part of the visual perception and synchronized with verbal information, express some subtle information that the voice or words alone cannot deliver. One of the most effective ways to improve presentation is to practice through feedback/suggestions by an expert. However, hiring human experts is expensive thus impractical most of the time. Towards this end, we propose a speech to gesture network (POSE) to generate exemplary body language given a vocal behavior speech as input. Specifically, we build an "expert" Speech-Gesture database based on the featured TED talk videos, and design a two-layer attentive recurrent encoder-decoder network to learn the translation from speech to gesture, as well as the hierarchical structure within gestures. Lastly, given a speech audio sequence, the appropriate gesture will be generated and visualized for a more effective communication. Both objective and subjective validation show the effectiveness of our proposed method.

As an intelligent way to interact with computers, the dialog system has been catching more and more attention. However, most research efforts only focus on text-based dialog systems, completely ignoring the rich semantics conveyed by the visual cues. Indeed, the desire for multimodal task-oriented dialog systems is growing with the rapid expansion of many domains, such as the online retailing and travel. Besides, few work considers the hierarchical product taxonomy and the users' attention to products explicitly. The fact is that users tend to express their attention to the semantic attributes of products such as color and style as the dialog goes on. Towards this end, in this work, we present a hierarchical User attention-guided Multimodal Dialog system, named UMD for short. UMD leverages a bidirectional Recurrent Neural Network to model the ongoing dialog between users and chatbots at a high level; As to the low level, the multimodal encoder and decoder are capable of encoding multimodal utterances and generating multimodal responses, respectively. The multimodal encoder learns the visual presentation of images with the help of a taxonomy-attribute combined tree, and then the visual features interact with textual features through an attention mechanism; whereas the multimodal decoder selects the required visual images and generates textual responses according to the dialog history. To evaluate our proposed model, we conduct extensive experiments on a public multimodal dialog dataset in the retailing domain. Experimental results demonstrate that our model outperforms the existing state-of-the-art methods by integrating the multimodal utterances and encoding the visual features based on the users' attribute-level attention.

Recently, as an essential part of people's daily life, clothing matching has gained increasing research attention. Most existing efforts focus on the numerical compatibility modeling between fashion items with advanced neural networks, and hence suffer from the poor interpretation, which makes them less applicable in real world applications. In fact, people prefer to know not only whether the given fashion items are compatible, but also the reasonable interpretations as well as suggestions regarding how to make the incompatible outfit harmonious. Considering that the research line of the comprehensively interpretable clothing matching is largely untapped, in this work, we propose a prototype-guided attribute-wise interpretable compatibility modeling (PAICM) scheme, which seamlessly integrates the latent compatible/incompatible prototype learning and compatibility modeling with the Bayesian personalized ranking (BPR) framework. In particular, the latent attribute interaction prototypes, learned by the non-negative matrix factorization (NMF), are treated as templates to interpret the discordant attribute and suggest the alternative item for each fashion item pair. Extensive experiments on the real-world dataset have demonstrated the effectiveness of our scheme.

Recently, due to the unprecedented growth of multimedia data, cross-modal hashing has gained increasing attention for the efficient cross-media retrieval. Typically, existing methods on cross-modal hashing treat labels of one instance independently but overlook the correlations among labels. Indeed, in many real-world scenarios, like the online fashion domain, instances (items) are labeled with a set of categories correlated by certain hierarchy. In this paper, we propose a new end-to-end solution for supervised cross-modal hashing, named HiCHNet, which explicitly exploits the hierarchical labels of instances. In particular, by the pre-established label hierarchy, we comprehensively characterize each modality of the instance with a set of layer-wise hash representations. In essence, hash codes are encouraged to not only preserve the layer-wise semantic similarities encoded by the label hierarchy, but also retain the hierarchical discriminative capabilities. Due to the lack of benchmark datasets, apart from adapting the existing dataset FashionVC from fashion domain, we create a dataset from the online fashion platform Ssense consisting of 15,696 image-text pairs labeled by 32 hierarchical categories. Extensive experiments on two real-world datasets demonstrate the superiority of our model over the state-of-the-art methods.

Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems assume that for an individual, this vector is the same for all items. However, this assumption is often invalid, especially when considering a user's interactions with items of diverse characteristics. To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items. Specifically, we design a new topic model to extract user preferences and item characteristics from review texts. They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network. Through extensive experiments on several large-scale datasets, we demonstrate that our model outperforms the state-of-the-art review-aware recommender systems in the rating prediction task.

In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific similarity structures into a collaborative similarity structure, and then perform the subsequent feature selection. These two processes are separate and independent. The collaborative similarity structure remains fixed during feature selection. Further, the simple undirected view combination may adversely reduce the reliability of the ultimate similarity structure for feature selection, as the view-specific similarity structures generally involve noises and outlying entries. To alleviate these problems, we propose an adaptive collaborative similarity learning (ACSL) for multi-view feature selection. We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework. Moreover, a reasonable rank constraint is devised to adaptively learn an ideal collaborative similarity structure with proper similarity combination weights and desirable neighbor assignment, both of which could positively facilitate the feature selection. An effective solution guaranteed with the proved convergence is derived to iteratively tackle the formulated optimization problem. Experiments demonstrate the superiority of the proposed approach.

Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.

Recently, the booming fashion sector and its huge potential benefits have attracted tremendous attention from many research communities. In particular, increasing research efforts have been dedicated to the complementary clothing matching as matching clothes to make a suitable outfit has become a daily headache for many people, especially those who do not have the sense of aesthetics. Thanks to the remarkable success of neural networks in various applications such as the image classification and speech recognition, the researchers are enabled to adopt the data-driven learning methods to analyze fashion items. Nevertheless, existing studies overlook the rich valuable knowledge (rules) accumulated in fashion domain, especially the rules regarding clothing matching. Towards this end, in this work, we shed light on the complementary clothing matching by integrating the advanced deep neural networks and the rich fashion domain knowledge. Considering that the rules can be fuzzy and different rules may have different confidence levels to different samples, we present a neural compatibility modeling scheme with attentive knowledge distillation based on the teacher-student network scheme. Extensive experiments on the real-world dataset show the superiority of our model over several state-of-the-art methods. Based upon the comparisons, we observe certain fashion insights that can add value to the fashion matching study. As a byproduct, we released the codes, and involved parameters to benefit other researchers.

The booming of social networks has given rise to a large volume of user-generated contents (UGCs), most of which are free and publicly available. A lot of users' personal aspects can be extracted from these UGCs to facilitate personalized applications as validated by many previous studies. Despite their value, UGCs can place users at high privacy risks, which thus far remains largely untapped. Privacy is defined as the individual's ability to control what information is disclosed, to whom, when and under what circumstances. As people and information both play significant roles, privacy has been elaborated as a boundary regulation process, where individuals regulate interaction with others by altering the openness degree of themselves to others. In this paper, we aim to reduce users' privacy risks on social networks by answering the question of Who Can See What. Towards this goal, we present a novel scheme, comprising of descriptive, predictive and prescriptive components. In particular, we first collect a set of posts and extract a group of privacy-oriented features to describe the posts. We then propose a novel taxonomy-guided multi-task learning model to predict which personal aspects are uncovered by the posts. Lastly, we construct standard guidelines by the user study with 400 users to regularize users' actions for preventing their privacy leakage. Extensive experiments on a real-world dataset well verified our scheme.

Nowadays, as a beauty-enhancing product, clothing plays an important role in human's social life. In fact, the key to a proper outfit usually lies in the harmonious clothing matching. Nevertheless, not everyone is good at clothing matching. Fortunately, with the proliferation of fashion-oriented online communities, fashion experts can publicly share their fashion tips by showcasing their outfit compositions, where each fashion item (e.g., a top or bottom) usually has an image and context metadata (e.g., title and category). Such rich fashion data offer us a new opportunity to investigate the code in clothing matching. However, challenges co-exist with opportunities. The first challenge lies in the complicated factors, such as color, material and shape, that affect the compatibility of fashion items. Second, as each fashion item involves multiple modalities (i.e., image and text), how to cope with the heterogeneous multi-modal data also poses a great challenge. Third, our pilot study shows that the composition relation between fashion items is rather sparse, which makes traditional matrix factorization methods not applicable. Towards this end, in this work, we propose a content-based neural scheme to model the compatibility between fashion items based on the Bayesian personalized ranking (BPR) framework. The scheme is able to jointly model the coherent relation between modalities of items and their implicit matching preference. Experiments verify the effectiveness of our scheme, and we deliver deep insights that can benefit future research.

Understanding the progression of chronic diseases can empower the sufferers in taking proactive care. To predict the disease status in the future time points, various machine learning approaches have been proposed. However, a few of them jointly consider the dual heterogeneities of chronic disease progression. In particular, the predicting task at each time point has features from multiple sources, and multiple tasks are related to each other in chronological order. To tackle this problem, we propose a novel and unified scheme to coregularize the prior knowledge of source consistency and temporal smoothness. We theoretically prove that our proposed model is a linear model. Before training our model, we adopt the matrix factorization approach to address the data missing problem. Extensive evaluations on real-world Alzheimer's disease data set have demonstrated the effectiveness and efficiency of our model. It is worth mentioning that our model is generally applicable to a rich range of chronic diseases.

Event-based social networking services, such as Meetup, are capable of linking online virtual interactions to offline physical activities. Compared to mono online social networking services (e.g., Twitter and Google+), such dual networks provide a complete picture of users’ online and offline behaviors that more often than not are compatible and complementary. In the light of this, we argue that joint learning over dual networks offers us a better way to comprehensively understand user behaviors and their underlying organizational principles. Despite its value, few efforts have been dedicated to jointly considering the following factors within a unified model: (1) local user contextualization, (2) global structure coherence, and (3) effectiveness evaluation. Toward this end, we propose a novel dual clustering model for community detection over dual networks to jointly model local consistency for a specific user and global consistency of partitioning results across networks. We theoretically derived its solution. In addition, we verified our model regarding multiple metrics from different aspects and applied it to the application of event attendance prediction.

Social networks contain a wealth of useful information. In this paper, we study a challenging task for integrating users' information from multiple heterogeneous social networks to gain a comprehensive understanding of users' interests and behaviors. Although much effort has been dedicated to study this problem, most existing approaches adopt linear or shallow models to fuse information from multiple sources. Such approaches cannot properly capture the complex nature of and relationships among different social networks. Adopting deep learning approaches to learning a joint representation can better capture the complexity, but this neglects measuring the level of confidence in each source and the consistency among different sources. In this paper, we present a framework for multiple social network learning, whose core is a novel model that fuses social networks using deep learning with source confidence and consistency regularization. To evaluate the model, we apply it to predict individuals' tendency to volunteerism. With extensive experimental evaluations, we demonstrate the effectiveness of our model, which outperforms several state-of-the-art approaches in terms of precision, recall and F1-score.

Micro-videos, a new form of user generated contents (UGCs), are gaining increasing enthusiasm. Popular micro-videos have enormous commercial potential in many ways, such as online marketing and brand tracking. In fact, the popularity prediction of traditional UGCs including tweets, web images, and long videos, has achieved good theoretical underpinnings and great practical success. However, little research has thus far been conducted to predict the popularity of the bite-sized videos. This task is non-trivial due to three reasons: 1) micro-videos are short in duration and of low quality; 2) they can be described by multiple heterogeneous channels, spanning from social, visual, acoustic to textual modalities; and 3) there are no available benchmark dataset and discriminant features that are suitable for this task. Towards this end, we present a transductive multi-modal learning model. The proposed model is designed to find the optimal latent common space, unifying and preserving information from different modalities, whereby micro-videos can be better represented. This latent space can be used to alleviate the information insufficiency problem caused by the brief nature of micro-videos. In addition, we built a benchmark dataset and extracted a rich set of popularity-oriented features to characterize the popular micro-videos. Extensive experiments have demonstrated the effectiveness of the proposed model. As a side contribution, we have released the dataset, codes and parameters to facilitate other researchers.

Volunteers have always been extremely crucial and in urgent need for nonprofit organizations (NPOs) to sustain their continuing operations. However, it is expensive and time-consuming to recruit volunteers using traditional approaches. In the Web 2.0 era, abundant and ubiquitous social media data opens a door to the possibility of automatic volunteer identification. In this article, we aim to fully explore this possibility by proposing a scheme that is able to predict users’ volunteerism tendency from user-generated contents collected from multiple social networks based on a conceptual volunteering decision model. We conducted comprehensive experiments to investigate the effectiveness of our proposed scheme and further discussed its generalizibility and extendability. This novel interdisciplinary research will potentially inspire more promising and important human-centered applications.

We are living in the era of social networks, where people throughout the world are connected and organized by multiple social networks. The views revealed by different social networks may vary according to the different services they offer. They are complimentary to each other and comprehensively characterize a specific user from different perspectives. As compared to the scare knowledge conveyed by a single source, appropriate aggregation of multiple social networks offers us a better opportunity for deep user understanding. The challenges, however, co-exist with opportunities. The first challenge lies in the existence of block-wise missing data, caused by the fact that some users may be very active in certain social networks while inactive in others. The second challenge is how to collaboratively integrate multiple social networks. Towards this end, we first proposed a novel model for data missing completion by seamlessly exploring the knowledge from multiple sources. We then developed a robust multiple social network learning model, and applied it to the application of volunteerism tendency prediction. Extensive experiments on real world dataset verify the effectiveness of our scheme. The proposed scheme is applicable to many other domains, such as demographic inference and interest prediction.

Volunteers are extremely crucial to nonprofit organizations (NPOs) to sustain their continuing operations. On the other hand, many talents are looking for appropriate volunteer opportunities to realize their dreams of making an impact on the world with their expertise. This is a typical supply and demand matching issue. Fortunately, user profiling and the discovery of user volunteering tendency can benefit from users' continuous enthusiasm and active participation in diverse online social networks (OSNs) and the huge amount of publicly available user generated contents (UGCs). In this work, we aim to bridge the gap between the supply of talents with volunteering tendency and the demands of social enterprise and enhance the social welfare. This is done by incorporating volunteering tendency into user profiling across multiple OSNs. Consequently, this interdisciplinary research opens a new window for both computer science and social science. To the best of our knowledge, this is the first attempt to tackle the problem of volunteer matching for social enterprise based on publicly available UGCs. First, we explain the definitions of the main concepts with examples. Second, we propose a system architecture for addressing the problem of volunteerism matching that Includes three components: Profile Collection, Profile Enrichment and Profile Matching. Finally, we identify the major challenges encountered in our current research work. This paper discusses our design and progress in this research.