In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently yields the best performance against state-of-the-art methods on fine-grained benchmark datasets.
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Supervised crowd counting relies on manual labeling, which is costly and time-consuming. This led to an increased interest in unsupervised methods. However, there is a significant domain gap issue in unsupervised methods, which is manifested by a model trained on one dataset serving dramatic performance drops when being transferred to another. This phenomenon can be attributed to the diverse domain knowledge making it difficult for the unsupervised models to transfer between general (e.g., similar distribution) and domain-specific (e.g., unique density, perspective, illumination, etc.) knowledge, leading to knowledge bias. Existing methods focus on exploring distinguishable relationships and establishing connections between the source and target domains. However, the similar knowledge transfer cannot perfectly simulate the contents of the target domain, leading to the model's inability to generalize to domain-specific knowledge. In this paper, we propose a Self-awareness Knowledge Diffusion method (SaKnD) that leverages the self-knowledge without establishing cross-domain knowledge relationships, which aims to balance the knowledge bias between general and domain-specific knowledge. Specifically, we propose a strategy to evaluate the uncertainty and consistency to define the clueless and informed areas, which determine the location and orientation of knowledge diffusion. These clueless areas serve as domain-specific knowledge that needs to be optimized, and these informed areas serve as general knowledge across domains. Extensive experiments on three standard crowd-counting benchmarks, ShanghaiTech PartA, ShanghaiTech PartB, and UCF_QNRF, show that the proposed SaKnD achieves state-of-the-art performance.
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.
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.
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.
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.
Prior image-text matching methods have shown remarkable performance on many benchmark datasets, but most of them overlook the bias in the dataset, which exists in intra-modal and inter-modal, and tend to learn the spurious correlations that extremely degrade the generalization ability of the model. Furthermore, these methods often incorporate biased external knowledge from large-scale datasets as prior knowledge into image-text matching model, which is inevitable to force model further learn biased associations. To address above limitations, this paper firstly utilizes Structural Causal Models (SCMs) to illustrate how intra- and inter-modal confounders damage the image-text matching. Then, we employ backdoor adjustment to propose an innovative Deconfounded Causal Inference Network (DCIN) for image-text matching task. DCIN (1) decomposes the intra- and inter-modal confounders and incorporates them into the encoding stage of visual and textual features, effectively eliminating the spurious correlations during image-text matching, and (2) uses causal inference to mitigate biases of external knowledge. Consequently, the model can learn causality instead of spurious correlations caused by dataset bias. Extensive experiments on two well-known benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superiority of our proposed method.
Visual Instance Retrieval (VIR) is a hot research topic for its wide application in real world, such as object re-identification in smart city scenarios. However, due to the limited style diversity in source training data, most existing VIR models always fail to generalize well to unseen domain. How to improve the generalizability of VIR models has received increasing attention in most recent years. In this paper, we pay attention to the Single Domain Generalization (SDG) based VIR task, a more challenging but practical problem, where the model is only trained on single domain data and directly evaluated on unseen target domain without any fine-tuning or adaptations. In this case, the limited style variance in training data may cause the model learning incorrect reliance on the superficial style feature and reduce the generalizability of the model. To address this issue, we propose a novel Style-Invariant robust Representation Learning (SIRL) method for the challenging task, which mainly aims to first diversify the training data with style augmentation, and then enforce the model to learn style-invariant features. Specifically, we first design an adversarial style synthesis module which learns to synthesize diverse augmented samples with adversarially learned styles. Then, we devise an invariant feature learning module to minimize cross-domain feature inconsistency between source images and style-augmented images for capturing domain-invariant instance features. In this way, we can prevent the model from over-exploiting semantic content-independent cues (e.g., color) as shortcut features, thereby estimating the pairwise instance similarity more robustly. We integrate our SIRL method with SOTA VIR networks and evaluate its effectiveness on several public benchmark datasets. Extensive experiments clearly show that the SIRL method can substantially improve the generalizability of existing VIR networks in the challenging SDG-VIR setting.
Existing methods for facial expression recognition (FER) lack the utilization of prior facial knowledge, primarily focusing on expression-related regions while disregarding explicitly processing expression-independent information. This paper proposes a patch-aware FER method that incorporates facial keypoints to guide the model and learns precise representations through two collaborative streams, addressing these issues. First, facial keypoints are detected using a facial landmark detection algorithm, and the facial image is divided into equal-sized patches using the Patch Embedding Module. Then, a correlation is established between the keypoints and patches using a simplified conversion relationship. Two collaborative streams are introduced, each corresponding to a specific mask strategy. The first stream masks patches corresponding to the keypoints, excluding those along the facial contour, with a certain probability. The resulting image embedding is input into the Encoder to obtain expression-related features. The features are passed through the Decoder and Classifier to reconstruct the masked patches and recognize the expression, respectively. The second stream masks patches corresponding to all the above keypoints. The resulting image embedding is input into the Encoder and Classifier successively, with the resulting logit approximating a uniform distribution. Through the first stream, the Encoder learns features in the regions related to expression, while the second stream enables the Encoder to better ignore expression-independent information, such as the background, facial contours, and hair. Experiments on two benchmark datasets demonstrate that the proposed method outperforms state-of-the-art methods.
Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.
Predicting individual aesthetic preferences holds significant practical applications and academic implications for human society. However, existing studies mainly focus on learning and predicting the commonality of facial attractiveness, with little attention given to Personalized Facial Beauty Prediction (PFBP). PFBP aims to develop a machine that can adapt to individual aesthetic preferences with only a few images rated by each user. In this paper, we formulate this task from a meta-learning perspective that each user corresponds to a meta-task. To address such PFBP task, we draw inspiration from the human aesthetic mechanism that visual aesthetics in society follows a Gaussian distribution, which motivates us to disentangle user preferences into a commonality and an individuality part. To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism. Unlike conventional meta-learning methods that may struggle with slow adaptation or overfitting to tiny support sets, we propose a novel approach that optimizes a high-order predictor for fast adaptation. In order to validate the performance of the proposed method, we build several PFBP benchmarks by using existing facial beauty prediction datasets rated by numerous users. Extensive experiments on these benchmarks demonstrate the effectiveness of the proposed MetaFBP method.
Most existing methods for image emotion classification extract features directly from images supervised by a single emotional label. However, this approach has a limitation known as the affective gap which restricts the capability of these features as they do not always align with the emotions perceived by users. To effectively bridge the affective gap, this paper proposes a visual content understanding network inspired by the human staged emotion perception process. The proposed network is comprised of three perception modules designed to extract multi-level information. Firstly, an entity perception module extracts entities from images. Secondly, an attribute perception module extracts the attribute content of each entity. Thirdly, an emotion perception module extracts emotion features based on both the entity and attribute information. We generate pseudo-labels of entities and attributes through image segmentation and vision-language models to provide auxiliary guidance for network learning. The progressive entity and attribute understanding enable the network to hierarchically extract semantic-level features for emotion analysis. Extensive experiments demonstrate that our progressive learning network achieves superior performance on various benchmark datasets for image emotion classification.
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation
PDF ↗Emotion Recognition in Conversation (ERC) plays an important role in driving the development of human-machine interaction. Emotions can exist in multiple modalities, and multimodal ERC mainly faces two problems: (1) the noise problem in the cross-modal information fusion process, and (2) the prediction problem of less sample emotion labels that are semantically similar but different categories. To address these issues and fully utilize the features of each modality, we adopted the following strategies: first, deep emotion cues extraction was performed on modalities with strong representation ability, and feature filters were designed as multimodal prompt information for modalities with weak representation ability. Then, we designed a Multimodal Prompt Transformer (MPT) to perform cross-modal information fusion. MPT embeds multimodal fusion information into each attention layer of the Transformer, allowing prompt information to participate in encoding textual features and being fused with multi-level textual information to obtain better multimodal fusion features. Finally, we used the Hybrid Contrastive Learning (HCL) strategy to optimize the model's ability to handle labels with few samples. This strategy uses unsupervised contrastive learning to improve the representation ability of multimodal fusion and supervised contrastive learning to mine the information of labels with few samples. Experimental results show that our proposed model outperforms state-of-the-art models in ERC on two benchmark datasets.
Domain adaptation has driven the progress of Facial Expression Recognition (FER). Existing cross-domain FER methods focus only on the domain alignment of a single source domain to the target domain, ignoring the importance of multisource domains that contain richer knowledge. However, Cross-Multidomain FER (CMFER)needs to combat the domain conflicts caused by the uncertainty of intradomain annotations and the inconsistency of interdomain distributions. To this end, this paper proposes a Domain-Uncertain Mutual Learning (DUML) method to deal with the more challenging CMFER problem. Specifically, we consider a domain-specific global perspective for domain-invariance representation and domain fusion for facial generic detail representation to mitigate cross-domain distribution differences. Further, we develop Intra-Domain Uncertainty (Intra-DU) and Inter-Domain Uncertainty (Inter-DU) to combat the large dataset shifts caused by annotation uncertainty. Finally, extensive experimental results on multiple benchmark across multidomain FER datasets demonstrate the remarkable effectiveness of DUML against CMFER uncertainty. All codes and training logs are publicly available at https://github.com/liuhw01/DUML.
Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimodal features and sentiment labels. For example, if most videos with a blue background have positive labels in a dataset, the model will rely on such correlations for prediction, while "blue background'' is not a sentiment-related feature. To address this problem, we define a general debiasing MSA task, which aims to enhance the Out-Of-Distribution (OOD) generalization ability of MSA models by reducing their reliance on spurious correlations. To this end, we propose a general debiasing framework based on Inverse Probability Weighting (IPW), which adaptively assigns small weights to the samples with larger bias (i.e., the severer spurious correlations). The key to this debiasing framework is to estimate the bias of each sample, which is achieved by two steps: 1) disentangling the robust features and biased features in each modality, and 2) utilizing the biased features to estimate the bias. Finally, we employ IPW to reduce the effects of large-biased samples, facilitating robust feature learning for sentiment prediction. To examine the model's generalization ability, we keep the original testing sets on two benchmarks and additionally construct multiple unimodal and multimodal OOD testing sets. The empirical results demonstrate the superior generalization ability of our proposed framework. We have released the code to facilitate the reproduction https://github.com/Teng-Sun/GEAR.
Large-scale pre-trained vision-language (VL) models have shown powerful generic representation capabilities for adapting to downstream tasks with limited training data, which are data-efficient solutions to various applications such as image recognition. In order to enhance the adaption performance, most existing methods attempt to introduce learnable vectors into the text prompt to generate adaptive classification weights for the class in the downstream task. However, they generally focus on the text side while neglecting adaptive visual feature generation on the image side, which is insufficient to fit the downstream task data. In this paper, we propose fine-grained visual prompt learning (FG-VPL) of vision-language models for image recognition with few training samples, and the main contributions are: (1) Fine-grained visual prompt is introduced into the image encoder of the vision-language model for focusing on the target object and conducting information interaction within the object, which facilitates generating discriminative visual features for image recognition. (2) A two-pathway adaptive recognition module is proposed to narrow the domain gap and utilize both the cross-modal knowledge of the vision-language model and the visual information of the few-sample training set for classifying images with the help of feature adapters. We conduct extensive experiments on 11 image recognition benchmark datasets under the few training samples setting, which demonstrate that our proposed approach can achieve state-of-the-art performance. The code is available at https://github.com/PKU-ICST-MIPL/FG-VPL_ACMMM2023.
MaTCR: Modality-Aligned Thought Chain Reasoning for Multimodal Task-Oriented Dialogue Generation
PDF ↗In recent years, multimodal task-oriented dialogue systems have attracted increasing attention from communities, owing to their ability to naturally and efficiently provide user service. Despite the commercial value of multimodal dialogue systems, they are still confronted with two challenges: (1) capture users' intention from lengthy context and side knowledge for question comprehension; (2) jointly consider the multimodal information for response generation. In view of the challenges, previous methods designed for specific scenario lack auxiliary reasoning structures with effective modality interaction, which hinders the comprehension of user's needs and impedes the generation of desired responses. To address these issues, we propose a Modality-aligned Thought Chain Reasoning (MaTCR) framework to insert explicit reasoning process for multimodal task-oriented dialogue generation. We construct a multimodal thought chain by summarizing intermediate user queries from aligned visual and textual context, which helps to guide the comprehension of user intentions for generating reasonable responses. To effectively extract and integrate multimodal information for high-quality thought chain reasoning, we design a multimodal reasoner consisting of visual representation learning and modality-aligned fusion. We comparatively justify MaTCR with several strong baselines, including the currently highly regarded large language model. Extensive experiments over a benchmark dataset demonstrate that MaTCR outperforms the existing methods and provides stronger interpretability.
Currently, multimedia systems and computer vision algorithms are increasingly playing a crucial role in biological research. However, due to the significant difference between macro and micro scenarios, it is impractical to directly transfer existing computer vision methods to the images captured by microscopes. Taking social behavior analysis of worm for example, it heavily depends on accurate and efficient Multi-object tracking (MOT) methods. Meanwhile, it faces great challenges due to the unique physical characteristics of worm, such as small size, highly uniform appearance, rapid deformation and overlapping movement. This paper studies on the challenges and existing solutions for MOT in worm crowds by building a well-designed dataset ("WormTrack") and a tracking-by-detection benchmark. We observed that the state-of-the-art MOT methods suffers from considerable performance drop on the new dataset. Therefore, we propose a customized MOT method for worm crowds by deeply understanding the physical characteristics of worms and scenes. The method is composed by an instance segmentation based detector, a multiple model fused Kalman filter based tracker and a multi-constraint based trajectory repairer. The experimental results demonstrate that our method can accurately track over 100 worms with almost identical appearance for a long period, which is exceptional compared to existing methods. We hope our work will attract further researches to explore more in this new field, and promote the crossing field researches with biology and medicine. Our code and data is available at https://github.com/Jeerrzy/wormstudio.
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
PDF ↗In the realm of facial analysis, accurate landmark detection is crucial for various applications, ranging from face recognition and expression analysis to animation. Conventional heatmap or coordinate regression-based techniques, however, often face challenges in terms of computational burden and quantization errors. To address these issues, we present the KeyPoint Positioning System (KeyPosS) - a groundbreaking facial landmark detection framework that stands out from existing methods. The framework utilizes a fully convolutional network to predict a distance map, which computes the distance between a Point of Interest (POI) and multiple anchor points. These anchor points are ingeniously harnessed to triangulate the POI's position through the True-range Multilateration algorithm. Notably, the plug-and-play nature of KeyPosS enables seamless integration into any decoding stage, ensuring a versatile and adaptable solution. We conducted a thorough evaluation of KeyPosS's performance by benchmarking it against state-of-the-art models on four different datasets. The results show that KeyPosS substantially outperforms leading methods in low-resolution settings while requiring a minimal time overhead.1 The code is available at https://github.com/zhiqic/KeyPosS.
Generating realistic 3D indoor scenes requires a deep understanding of objects and their spatial relationships. However, existing methods often fail to generate realistic 3D scenes due to the limited understanding of object relationships. To tackle this problem, we propose a Scene Graph Masked Variational Auto-Encoder (SG-MVAE) framework that fully captures the relationships between objects to generate more realistic 3D scenes. Specifically, we first introduce a relationship completion module that adaptively learns the missing relationships between objects in the scene graph. To accurately predict the missing relationships, we employ multi-group attention to capture the correlations between the objects with missing relationships and other objects in the scene. After obtaining the complete scene relationships, we mask the relationships between objects and use a decoder to reconstruct the scene. The reconstruction process enhances the model's understanding of relationships, generating more realistic scenes. Extensive experiments on benchmark datasets show that our model outperforms state-of-the-art methods.