Taxonomy expansion task is essential in organizing the ever-increasing volume of new concepts into existing taxonomies. Most existing methods focus exclusively on using textual semantics, leading to an inability to generalize to unseen terms and the "Prototypical Hypernym Problem." In this paper, we propose Visual Taxonomy Expansion (VTE), introducing visual features into the taxonomy expansion task. We propose a textual hypernymy learning task and a visual prototype learning task to cluster textual and visual semantics. In addition to the tasks on respective modalities, we introduce a hyper-proto constraint that integrates textual and visual semantics to produce fine-grained visual semantics. Our method is evaluated on two datasets, where we obtain compelling results. Specifically, on the Chinese taxonomy dataset, our method significantly improves accuracy by 8.75%. Additionally, our approach performs better than ChatGPT on the Chinese taxonomy dataset.
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Interactive Interior Design Recommendation via Coarse-to-fine Multimodal Reinforcement Learning
PDF ↗Personalized interior decoration design often incurs high labor costs. Recent efforts in developing intelligent interior design systems have focused on generating textual requirement-based decoration designs while neglecting the problem of how to mine homeowner's hidden preferences and choose the proper initial design. To fill this gap, we propose an Interactive Interior Design Recommendation System (IIDRS) based on reinforcement learning (RL). IIDRS aims to find an ideal plan by interacting with the user, who provides feedback on the gap between the recommended plan and their ideal one. To improve decision-making efficiency and effectiveness in large decoration spaces, we propose a Decoration Recommendation Coarse-to-Fine Policy Network (DecorRCFN). Additionally, to enhance generalization in online scenarios, we propose an object-aware feedback generation method that augments model training with diversified and dynamic textual feedback. Extensive experiments on a real-world dataset demonstrate our method outperforms traditional methods by a large margin in terms of recommendation accuracy. Further user studies demonstrate that our method reaches higher real-world user satisfaction than baseline methods.
Person re-identification (Re-ID) has been extensively investigated in recent years. However, many existing paradigms rely on holistic person regions for matching, disregarding the challenges posed by occlusions in real-world scenarios. Recent methods have explored occlusion augmentation or external semantic cues. Nevertheless, these approaches tend to be coarse-grained, discarding valuable semantic information in local regions when determining them as occlusions. In this paper, we propose a Fine-grained Occlusion Disentanglement Network (FODN) that can extract more information from limited person regions. Specifically, we propose a fine-grained occlusion augmentation scheme to generate diverse occlusion data and employ bilinear interpolation and downsampling strategies to obtain fine-grained occlusion labels. We then design an occlusion feature disentanglement Module that decouples norm and angle from features and supervises the occlusion-aware task using the aforementioned occlusion labeling and person re-identification tasks, respectively, resulting in more robust features. Additionally, we propose a dynamic local weight controller to balance the relative importance of various human body parts, thereby improving the model's ability to mine more effective local features from limited human body regions after occlusion removal. Comprehensive experiments on various person Re-ID benchmarks demonstrate the superiority of FODN over state-of-the-art methods.
Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge. However, existing methods still lack domain knowledge and struggle to capture users' fine-grained preferences. Meanwhile, many traditional SR methods improve this issue by integrating side information while suffering from information loss. To summarize, we believe that a good recommendation system should utilize both general and domain knowledge simultaneously. Therefore, we introduce an external knowledge base and propose Knowledge Prompt-tuning for Sequential Recommendation (KP4SR). Specifically, we construct a set of relationship templates and transform a structured knowledge graph (KG) into knowledge prompts to solve the problem of the semantic gap. However, knowledge prompts disrupt the original data structure and introduce a significant amount of noise. We further construct a knowledge tree and propose a knowledge tree mask, which restores the data structure in a mask matrix form, thus mitigating the noise problem. We evaluate KP4SR on three real-world datasets, and experimental results show that our approach outperforms state-of-the-art methods on multiple evaluation metrics. Specifically, compared with PLM-based methods, our method improves NDCG@5 and HR@5 by 40.65% and 36.42% on the books dataset, 11.17% and 11.47% on the music dataset, and 22.17% and 19.14% on the movies dataset, respectively. Our code is publicly available at the link: https://github.com/zhaijianyang/KP4SR.
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model
PDF ↗In this era of videos, automatic video editing techniques attract more and more attention from industry and academia since they can reduce workloads and lower the requirements for human editors. Existing automatic editing systems are mainly scene-or event-specific, e.g., soccer game broadcasting, yet the automatic systems for general editing, e.g., movie or vlog editing which covers various scenes and events, were rarely studied before, and converting the event-driven editing method to a general scene is nontrivial. In this paper, we propose a two-stage scheme for general editing. Firstly, unlike previous works that extract scene-specific features, we leverage the pre-trained Vision-Language Model (VLM) to extract the editing-relevant representations as editing context. Moreover, to close the gap between the professional-looking videos and the automatic productions generated with simple guidelines, we propose a Reinforcement Learning (RL)-based editing framework to formulate the editing problem and train the virtual editor to make better sequential editing decisions. Finally, we evaluate the proposed method on a more general editing task with a real movie dataset. Experimental results demonstrate the effectiveness and benefits of the proposed context representation and the learning ability of our RL-based editing framework.
This paper introduces ChinaOpen, a dataset sourced from Bilibili, a popular Chinese video-sharing website, for open-world multimodal learning. While the state-of-the-art multimodal learning networks have shown impressive performance in automated video annotation and cross-modal video retrieval, their training and evaluation are primarily conducted on YouTube videos with English text. Their effectiveness on Chinese data remains to be verified. In order to support multimodal learning in the new context, we construct ChinaOpen-50k, a webly annotated training set of 50k Bilibili videos associated with user-generated titles and tags. Both text-based and content-based data cleaning are performed to remove low-quality videos in advance. For a multi-faceted evaluation, we build ChinaOpen-1k, a manually labeled test set of 1k videos. Each test video is accompanied with a manually checked user title and a manually written caption. Besides, each video is manually tagged to describe objects / actions / scenes shown in the visual content. The original user tags are also manually checked. Moreover, with all the Chinese text translated into English, ChinaOpen-1k is also suited for evaluating models trained on English data. In addition to ChinaOpen, we propose Generative Video-to-text Transformer (GVT) for Chinese video captioning. We conduct an extensive evaluation of the state-of-the-art single-task / multi-task models on the new dataset, resulting in a number of novel findings and insights.
The recent trend in unsupervised hashing requires not only a discrete representation space but also the ability to mine the similarities between data points. Determining and maintaining the relations between each datum and all the others maximally utilize the semantic diversity of the training set, but can we explore this on the holistic dataset using a single network end-to-end? In this paper, we take a step towards this vision by proposing Overview Hashing (OH). OH unifies the two ultimate goals of unsupervised hashing, i.e., (1) encoding compact features and (2) learning data similarities on a large scale end-to-end, into one model. In particular, we split the top of an encoder into a binary hash head and a continuous one. For an arbitrary datum, its similarities to all the others in the dataset are reflected in the Hamming distances of their hash heads. The distances then act as the weights to aggregate the continuous heads, shaping the final representation of this datum for loss computation. Hence, training with this representation simultaneously tunes the similarities of this datum to the whole dataset. In the context of a contrastive learning framework, we theoretically endorse our design by linking it to knowledge distillation and the attention mechanisms. Our experiments on the benchmarked datasets show the superiority of OH over the state-of-the-art hashing methods. Code is available at \hrefhttps://github.com/RosieYuu/OH \textcolorred https://github.com/RosieYuu/OH.
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations introduce spurious correlations that fail to reveal users' true preferences. Existing works attempt to alleviate this problem by learning invariant representations, but overlook the balance between independent and identically distributed (IID) and out-of-distribution (OOD) generalization. In this paper, we propose a framework called Pareto Invariant Representation Learning (PaInvRL) to mitigate the impact of spurious correlations from an IID-OOD multi-objective optimization perspective, by learning invariant representations (intrinsic factors that attract user attention) and variant representations (other factors) simultaneously. Specifically, PaInvRL includes three iteratively executed modules: (i) heterogeneous identification module, which identifies the heterogeneous environments to reflect distributional shifts for user-item interactions; (ii) invariant mask generation module, which learns invariant masks based on the Pareto-optimal solutions that minimize the adaptive weighted Invariant Risk Minimization (IRM) and Empirical Risk (ERM) losses; (iii) convert module, which generates both variant representations and item-invariant representations for training a multi-modal recommendation model that mitigates spurious correlations and balances the generalization performance within and cross the environmental distributions. We compare the proposed PaInvRL with state-of-the-art recommendation models on three public multimedia recommendation datasets (Movielens, Tiktok, and Kwai), and the experimental results validate the effectiveness of PaInvRL for both within-and cross-environmental learning.
Image-to-image retrieval, a fundamental task, aims at matching similar images based on a query image. Existing methods with convolutional neural networks are usually sensitive to low-level visual features, and ignore high-level semantic relationship information. This makes retrieving complicated images with multiple objects and various relationships a significant challenge. Although some works introduce the scene graph to capture the global semantic features of the objects and their relations, they ignore the local visual representations. In addition, due to the fragility of individual modal representations, poisoning attacks in adversarial scenarios are easily achieved, hurting the robustness of the visual-guided foundation image retrieval model. To overcome these issues, we propose a novel hierarchical semantic-guided image-to-image retrieval method via scene graph, called Hi-SIGIR. Specifically, to begin with, our proposed method generates the scene graph of an image. Then, our model extracts and learns both the visual and semantic features of the nodes and relations within the scene graphs. Next, these features are fused to obtain local information and sent to the graph neural network to obtain global information. Using these information, the similarity between the scene graphs of several images is calculated at both the local and global levels to perform image retrieval. Finally, we introduce a surrogate that calculates relevance in a cross-modal manner to understand image content better. Experimental evaluations on several wildly-used benchmarks demonstrate the superiority of the proposed method.
Due to the progress of large-scale multimodal model pretraining, existing cross-modal retrieval techniques is accurate to align text description to the target image when they show close and clear semantic correspondence. However, in real situations, users only provide ambiguous text queries, making it difficult to retrieve the desired images. To address this issue, we introduce the conversational composed retrieval paradigm, inspired by conversational search which models complex user intent through iterative interaction. This paradigm enhances the model capacity in learning fine-grained correspondences. To train the cross-modal conversational retrieval, we propose the Iterative Refining Retrieval (IRR) framework. It formalizes the reference images and modification texts in each session as a multimodal sequence, which is fed into the generative model to predict the information in the sequence autoregressively, and ultimately predicting the target image feature. In the conversational retrieval paradigm, the model refines the learned correspondences based on the interaction in the later stage of the retrieval session, thus captures fine-grained semantic correspondence to enforce the cross-modal representation. We propose a domain-specific multimodal pretraining method and the full sequence sampling augmentation method to fully utilize the session information. Extensive experiments demonstrate that the iterative refining retrieval method achieves state-of-the-art performance on sessions of varying lengths.
Deep hashing has been widely applied in large-scale image retrieval due to its powerful computational efficiency. Nevertheless, the vulnerability of deep hashing to adversarial examples has been revealed, particularly to targeted attacks with stronger manipulability. Existing targeted attack methods for deep hashing default to selecting target labels from random images, usually encompassing multiple classes for attack in multi-label datasets. However, they exhabit poor performance when facing a preciser single target label selection. In this work, we propose a novel Precise Target-Oriented Attack dubbed PTA, to enhance the precision of such targeted attacks. Specifically, we further categorize the general target label into preciser single target label for attack. By relaxing the non-differentiable indicator function, we directly adopt Average Precision (AP) as optimization objective to guide the generation of adversarial examples on a small subset of the entire database, thus achieving stronger precision. Extensive experiments demonstrate that the proposed PTA achieves state-of-the-art performance in both general and single target label selection, with superior transferability and universality.
Multimodal recommendation system has been widely used in short video platform, e-commerce platform and news media. Multimodal data contains information such as product image and product text, which is often used as auxiliary signal to improve the effect of recommendation system significantly. In order to alleviate the problems of data sparsity and noise, some researchers construct data augmentation to use self-supervised learning to help model training. These methods have achieved certain results. However, most of the work is based on data augmentation in random ways, such as random masking and random perturbation. This random method is likely to lose important information and introduce new noise, resulting in biased augmentation data. Therefore, we propose a Modal-aware Bias Constrained Contrastive Learning method (BCCL) to solve the above problems. Specifically, BCCL introduces a bias-constrained data augmentation method to ensure the quality of augmentation samples. Then the multi-modal semantic information is modeled by the designed modal awareness module. Furthermore, we propose a information alignment module to improve the sparse modal feature learning of the model. We conducted a comprehensive experiment on three real-world data sets, and the experimental results showed that the proposed BCCL outperformed all the state-of-art methods. In-depth experiments have verified the effectiveness of our proposed modules.
Image-text matching is a hot topic in multi-modal analysis. The existing image-text matching algorithms focus on bridging the heterogeneity gap and mapping the feature into a common space under strong alignment assumption. However, these methods have unsatisfactory performance under the weak alignment scenario, which assumes that the text contains more abstract information, and the number of entities in the text is always fewer than objects in image. This is the first time, from our knowledge, to solve the image-text matching problem from the perspective of information difference with weak alignment. In order to both narrow the cross-modal heterogeneity gap and balance the information discrepancy, we proposed an imagination network to enrich the text modality based on pre-trained framework, which is helpful for image-text matching. The imagination network utilizes reinforcement learning to enhance the semantic information for text modality, and an action refinement strategy is designed to constrain the freedom and divergence of imagination. The experiment results show the superiority and generality of the proposed framework based on two pre-trained models, CLIP and BLIP on two most frequently-used datasets MSCOCO and Flickr30K.
By noticing the superior ability of contrastive learning in representation learning, several recent works have proposed to use it to learn semantic-rich hash codes. However, due to the absence of label information, existing contrastive-based hashing methods simply follow contrastive learning by only using the augmentation of the anchor as positive, while treating all other samples in the batch as negatives, resulting in the ignorance of a large number of potential positives. Consequently, the learned hash codes tend to be distributed dispersedly in the space, making their distances unable to accurately reflect their semantic similarities. To address this issue, we propose to exploit the similarity knowledge and hidden structure of the dataset. Specifically, we first develop an intuitive approach based on self-training that comprises two main components, a pseudo-label predictor and a hash code improving module, which mutually benefit from each other by utilizing the output from one another, in conjunction with the similarity knowledge obtained from pre-trained models. Furthermore, we subjected the intuitive approach to a more rigorous probabilistic framework and propose CGHash, a probabilistic hashing model based on conditional generative models, which is theoretically more reasonable and could model the similarity knowledge and the hidden group structure more accurately. Our extensive experimental results on three image datasets demonstrate that CGHash exhibits significant superiority when compared to both the proposed intuitive approach and existing baselines. Our code is available at https://github.com/KARLSZP/CGHash.
With the continuous generation of massive amounts of multimedia data nowadays, hashing has demonstrated significant potentials for large-scale search. To handle the emerging needs for streaming data retrieval, online hashing is drawing more and more attention. For online scenario, data distribution may change and concept drifts may occur as new data is continuously added to the database. Inevitably, hashing models may lose or disrupt the previously obtained knowledge when learning from new information, which is called the problem of catastrophic forgetting. In this paper, we propose a new online hashing method called Self-distillation Dual-memory Online Hashing with Hash Centers, which is abbreviated to SDOH-HC, to overcome this challenge. Specifically, SDOH-HC contains replay and distillation modules. For replay, a dual-memory mechanism is proposed which involves hash centers and exemplars. For knowledge distillation, we let hash centers distill information from themselves, i.e., the version of last round. Additionally, a new objective function is further built on above modules and is solved discretely to learn hash codes. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method.
Cross-Domain Recommendation (CDR) is proposed to address the long-standing data sparsity problem in recommender systems (RSs). Traditional CDR only leverages relatively richer information from an auxiliary domain to improve the performance in a sparser domain, which is also called single-target CDR. In recent years, dual-target CDR has been proposed to improve recommendation performance in both domains simultaneously. The existing dual-target CDR methods are based on common users to achieve knowledge transfer between domains. We argue that the existing methods face two challenges: (1) how to learn more representative user and item embeddings in each domain, and (2) in the case of a small number of common users in real-world datasets, how to achieve better knowledge transfer. To address these challenges, in this paper, we propose a contrastive learning (CL) framework, called CL-DTCDR. In CL-DTCDR, we first design a CL task in each domain to learn more representative user and item embeddings. Then, we further construct positive pairs of the user and her/his most similar user between domains to optimize user embeddings. By two CL tasks, CL-DTCDR effectively improves performance in both domains. Extensive experiments conducted on three real-world datasets demonstrate that CL-DTCDR significantly outperforms the state-of-the-art approaches.
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short. To tackle this problem, we focus on Multi-Behavior Sequential Recommendation (MBSR) in this paper, which aims to leverage time-evolving heterogeneous behavioral dependencies for better exploring users' potential intents on the target behavior. Solving MBSR is challenging. On the one hand, users exhibit diverse multi-behavior patterns due to personal characteristics. On the other hand, there exists comprehensive co-influence between behavior correlations and item collaborations, the intensity of which is deeply affected by temporal factors. To tackle these challenges, we propose a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem, which models personalized patterns and multifaceted sequential collaborations in a novel way to boost recommendation performance. PBAT includes two main modules, i.e, personalized behavior pattern generator and behavior-aware collaboration extractor. First, PBAT develops a personalized behavior pattern generator in the representation layer, which extracts dynamic and discriminative behavior patterns for sequential learning. Second, PBAT reforms the self-attention layer with a behavior-aware collaboration extractor, which introduces a fused behavior-aware attention mechanism for incorporating both behavioral and temporal impacts into collaborative transitions. We conduct experiments on three benchmark datasets and the results demonstrate the effectiveness and interpretability of our framework.
Multimodal contrastive learning aims to train a general-purpose feature extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text data. This can greatly benefit various complex downstream tasks, including cross-modal image-text retrieval and image classification. Despite its promising prospect, the security issue of cross-modal pre-trained encoder has not been fully explored yet, especially when the pre-trained encoder is publicly available for commercial use. In this work, we propose AdvCLIP, the first attack framework for generating downstream-agnostic adversarial examples based on cross-modal pre-trained encoders. AdvCLIP aims to construct a universal adversarial patch for a set of natural images that can fool all the downstream tasks inheriting the victim cross-modal pre-trained encoder. To address the challenges of heterogeneity between different modalities and unknown downstream tasks, we first build a topological graph structure to capture the relevant positions between target samples and their neighbors. Then, we design a topology-deviation based generative adversarial network to generate a universal adversarial patch. By adding the patch to images, we minimize their embeddings similarity to different modality and perturb the sample distribution in the feature space, achieving unviersal non-targeted attacks. Our results demonstrate the excellent attack performance of AdvCLIP on two types of downstream tasks across eight datasets. We also tailor three popular defenses to mitigate AdvCLIP, highlighting the need for new defense mechanisms to defend cross-modal pre-trained encoders. Our codes are available at: https://github.com/CGCL-codes/AdvCLIP.
Pattern images are artificially designed images that possess distinctiveness in their elements, styles, and arrangements. With the ever-growing number of pattern images, pattern image retrieval emerges as a promising technique with significant potential for commercial and industrial applications, such as fashion and home decoration, facilitating rapid identification of preferred print patterns by users. The main purpose of multi-label pattern image retrieval is to effectively represent and match images with their corresponding labels. Compared to conventional image retrieval, multi-label pattern image retrieval faces greater challenges due to the richer semantic information contained within the abstract print patterns and the complex relationships between multiple labels. To tackle these challenges, we propose a model specifically designed for multi-label pattern image retrieval, called Tran-GCN. Our proposed model is built upon a Transformer-based autoregressive architecture, which leverages image information to guide the exploration of correlations between different labels through the textual modality. By utilizing this correlation information, we construct a graph convolutional network (GCN) model to further enhance the correlations between image and label representations. To be more specific, our Tran-GCN model utilizes a cross-modal attention mechanisms at each layer to effectively aggregate visual features from the input image and update label semantics through residual connections. The GCN module is updated based on the correlation between textual features, as represented in a relationship matrix. Extensive experiments on two widely used public visual benchmarks, MS-COCO and NUS-WIDE, as well as a multi-label pattern image dataset, Pattern 2, consistently demonstrate the ability of our proposed Tran-GCN model for general use and its superior performance in multi-label pattern image retrieval tasks as well.
Text-based person retrieval aims at searching for a pedestrian image from multiple candidates with textual descriptions. It is challenging due to uncertain cross-modal alignments caused by the large intra-class variations. To address the challenge, most existing approaches rely on various attention mechanisms and auxiliary information, yet still struggle with the uncertain cross-modal alignments arising from significant intra-class variation, leading to coarse retrieval results. To this end, we propose a novel framework termed Deep Cross-modal Evidential Learning (DCEL), which deploys evidential deep learning to consider the cross-modal alignment uncertainty. Our DCEL model comprises three components: (1) Bidirectional Evidential Learning, which models alignment uncertainty to measure and mitigate the influence of large intra-class variation; (2) Multi-level Semantic Alignment, which leverages a proposed Semantic Filtration module and image-text similarity distribution to facilitate cross-modal alignments; (3) Cross-modal Relation Learning, which reasons about latent correspondences between multi-level tokens of image and text. Finally, we integrate the advantages of the three proposed components to enhance the model to achieve reliable cross-modal alignments. Our DCEL method consistently outperforms more than ten state-of-the-art methods in supervised, weakly supervised, and domain generalization settings on three benchmarks: CUHK-PEDES, ICFG-PEDES, and RSTPReid.