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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.