Multimodal physiological-based emotion recognition is one of the most available but challenging studies due to complexity of emotions and individual differences in physiological signals. However, existing studies mainly combine multimodal data to fuse multimodal information in offline scenarios, ignoring data/modalities correlation among multimodal data and individual differences of non-stationary physiological signals in online scenarios. In this paper, we propose a novel Online Multimodal HyperGraph Learning (OMHGL) method to fuse multimodal information for emotion recognition based on time-series physiological signals. Our method consists of multimodal hypergraph fusion and online hypergraph learning. Specifically, the multimodal hypergraph fusion can fuse multimodal physiological signals to effectively obtain emotionally dependent information via leveraging multimodal information and higher-order correlations among multimodal data/modalities. The online hypergraph learning is designed to learn new information from online data by updating hypergraph projection. As a result, the proposed online emotion recognition model can be more effective for emotion recognition of target subjects when target data arrive in an online manner. Experimental results have demonstrated that the proposed method significantly outperforms the baselines and compared state-of-the-art methods in online emotion recognition tasks.
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The present notion of visual similarity is based on features derived from image contents. This ignores the users' emotional or affective experiences toward the content, and how users feel when they search for images. Here we consider valence, a positive or negative quantification of affective appraisal, as a novel dimension of image similarity. We report the largest neuroimaging experiment that quantifies and predicts the valence of visual content by using functional near-infrared spectroscopy from brain-computer interfacing. We show that affective similarity can be (1)~decoded directly from brain signals in response to visual stimuli, (2)~utilized for predicting affective image similarity with an average accuracy of 0.58 and an accuracy of 0.65 for high-arousal stimuli, and (3)~effectively used to complement affective similarity estimates of content-based models; for example when fused fNIRS and image rankings the retrieval F-measure@20 is 0.70. Our work opens new research avenues for affective multimedia analysis, retrieval, and user modeling.
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.
6D pose estimation from a single RGB image is a fundamental task in computer vision. In most methods of instance-level or category-level 6D pose estimation, accurate CAD models or point cloud models are indispensable part. It is not easy to quickly obtain the models of these everyday objects. To address this issue, we present a part-level object component sketch knowledge base which consists of 270 real-world object sketch models of 30 categories. Objects are disassembled into geometry components with spatial relationship according to their functions and structures, and convert them into three basic spatial structures: frustum, circular truncated cone, and sphere. We present a fast pipeline for sketch modeling with our tool. The average time for this method to build a simple model for everyday objects is about 2 minutes. Additionally, we leverage the geometric information and spatial relationships inherent in the multiple viewpoint projection maps of these sketch bases to develop a rapid inference framework for 6D pose estimation. The interpretable steps in our framework gradually retrieve and activate valid solutions in the discrete 6D pose space. Extensive experiments in real-world environments have demonstrated that our method can reliably and robustly estimate the 6D pose of objects, even without access to accurate CAD or point cloud models. Furthermore, our method achieves state-of-the-art performance, operating at a speed of 90 frames per second using parallel computing on GPU.
In this paper, we study the task of referring semantic segmentation in a highly practical setting, in which labeled visual data with corresponding text descriptions are available in the source, but only unlabeled visual data (without text descriptions) are available in the target. It is a challenging task that has many difficulties: (1) how to obtain proper queries for the target domain; (2) how to adapt visual-text joint distribution shifts; (3) how to maintain the original segmentation performance. Thus, we propose a cycle-consistent vision-language matching network to narrow down the domain gap and ease adaptation difficulty. Our model has significant practical applications since they are capable generalising to new data sources without requiring corresponding text annotations. First, a pseudo-text selector is devised to handle the missing modality, through the pre-trained clip model to measure the gap between query features of the source and visual features of the target. Next, a cross-domain segmentation predictor is adopted, which prompts the joint representations to be domain invariant and minimize the discrepancy between two domains. Then, we present a cycle-consistent query matcher to learn discriminative features via reconstructing visual features from masks. Instead of doing the textual comparison, we match the visual features to the pseudo queries. Extensive experiments show the effectiveness of our method.
Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-engineered prompts as trainable parameters. Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning. In this work, we present Visual Prompt Adaptation (VPA), the first framework that generalizes visual prompting with test-time adaptation. VPA introduces a small number of learnable tokens, enabling fully test-time and storage-efficient adaptation without necessitating source-domain information. We examine our VPA design under diverse adaptation settings, encompassing single-image, batched-image, and pseudo-label adaptation. We evaluate VPA on multiple tasks, including out-of-distribution (OOD) generalization, corruption robustness, and domain adaptation. Experimental results reveal that VPA effectively enhances OOD generalization by 3.3% across various models, surpassing previous test-time approaches. Furthermore, we show that VPA improves corruption robustness by 6.5% compared to strong baselines. Finally, we demonstrate that VPA also boosts domain adaptation performance by relatively 5.2%. Our VPA also exhibits marked effectiveness in improving the robustness of zero-shot recognition for vision-language models.
Pseudo-Labeling based semi-supervised learning has shown promising advantages in Scene Text Recognition (STR). Most of them usually use a pre-trained model to generate sequence-level pseudo labels for text images and then re-train the model. Recently, conducting Pseudo-Labeling in a teacher-student framework (a student model is supervised by the pseudo labels from a teacher model) has become increasingly popular, which trains in an end-to-end manner and yields outstanding performance in semi-supervised learning. However, applying this framework directly to Pseudo-Labeling STR exhibits unstable convergence, as generating pseudo labels at the coarse-grained sequence-level leads to inefficient utilization of unlabelled data. Furthermore, the inherent domain shift between labeled and unlabeled data results in low quality of derived pseudo labels. To mitigate the above issues, we propose a novel Cross-domain Pseudo-Labeling (CPL) approach for scene text recognition, which makes better utilization of unlabeled data at the character-level and provides more accurate pseudo labels. Specifically, our proposed Pseudo-Labeled Curriculum Learning dynamically adjusts the thresholds for different character classes according to the model's learning status. Moreover, an Adaptive Distribution Regularizer is employed to bridge the domain gap and improve the quality of pseudo labels. Extensive experiments show that CPL boosts those representative STR models to achieve state-of-the-art results on six challenging STR benchmarks. Besides, it can be effectively generalized to handwritten text.
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.
Context-aware methods achieved great success in supervised scene text recognition via incorporating semantic priors from words. We argue that such prior contextual information can be interpreted as the relations of textual primitives due to the heterogeneous text and background, which can provide effective self-supervised labels for representation learning. However, textual relations are restricted to the finite size of dataset due to lexical dependencies, which causes the problem of over-fitting and compromises representation robustness. To this end, we propose to enrich the textual relations via rearrangement, hierarchy and interaction, and design a unified framework called RCLSTR: Relational Contrastive Learning for Scene Text Recognition. Based on causality, we theoretically explain that three modules suppress the bias caused by the contextual prior and thus guarantee representation robustness. Experiments on representation quality show that our method outperforms state-of-the-art self-supervised STR methods. Code is available at https://github.com/ThunderVVV/RCLSTR.
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.
Large-scale pre-trained vision-language models allow for the zero-shot text-based generation of 3D avatars. The previous state-of-the-art method utilized CLIP to supervise neural implicit models that reconstructed a human body mesh. However, this approach has two limitations. Firstly, the lack of avatar-specific models can cause facial distortion and unrealistic clothing in the generated avatars. Secondly, CLIP only provides optimization direction for the overall appearance, resulting in less impressive results. To address these limitations, we propose AvatarFusion, the first framework to use a latent diffusion model to provide pixel-level guidance for generating human-realistic avatars while simultaneously segmenting clothing from the avatar's body. AvatarFusion includes the first clothing-decoupled neural implicit avatar model that employs a novel Dual Volume Rendering strategy to render the decoupled skin and clothing sub-models in one space. We also introduce a novel optimization method, called Pixel-Semantics Difference-Sampling (PS-DS), which semantically separates the generation of body and clothes, and generates a variety of clothing styles. Moreover, we establish the first benchmark for zero-shot text-to-avatar generation. Our experimental results demonstrate that our framework outperforms previous approaches, with significant improvements observed in all metrics. Additionally, since our model is clothing-decoupled, we can exchange the clothes of avatars. Code are available on our project page https://hansenhuang0823.github.io/AvatarFusion.
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.
Animal action recognition has a wide range of applications. However, the field largely remains unexplored due to the greater challenges compared to human action recognition, such as lack of annotated training data, large intra-class variation, and interference of cluttered background. Most of the existing methods directly apply human action recognition techniques, which essentially require a large amount of annotated data. In recent years, contrastive vision-language pretraining has demonstrated strong zero-shot generalization ability and has been used for human action recognition. Inspired by the success, we develop a highly performant action recognition framework based on the CLIP model. Our model addresses the above challenges via a novel category-specific prompting module to generate adaptive prompts for both text and video based on the animal category detected in input videos. On one hand, it can generate more precise and customized textual descriptions for each action and animal category pair, being helpful in the alignment of textual and visual space. On the other hand, it allows the model to focus on video features of the target animal in the video and reduce the interference of video background noise. Experimental results demonstrate that our method outperforms five previous action recognition methods on the Animal Kingdom dataset and has shown best generalization ability on unseen animals.
Stylized visual captioning aims to generate image or video descriptions with specific styles, making them more attractive and emotionally appropriate. One major challenge with this task is the lack of paired stylized captions for visual content, so most existing works focus on unsupervised methods that do not rely on parallel datasets. However, these approaches still require training with sufficient examples that have style labels, and the generated captions are limited to predefined styles. To address these limitations, we explore the problem of Few-Shot Stylized Visual Captioning, which aims to generate captions in any desired style, using only a few examples as guidance during inference, without requiring further training. We propose a framework called FS-StyleCap for this task, which utilizes a conditional encoder-decoder language model and a visual projection module. Our two-step training scheme proceeds as follows: first, we train a style extractor to generate style representations on an unlabeled text-only corpus. Then, we freeze the extractor and enable our decoder to generate stylized descriptions based on the extracted style vector and projected visual content vectors. During inference, our model can generate desired stylized captions by deriving the style representation from user-supplied examples. Our automatic evaluation results for few-shot sentimental visual captioning outperform state-of-the-art approaches and are comparable to models that are fully trained on labeled style corpora. Human evaluations further confirm our model's ability to handle multiple styles.
Automatic detection of multimodal fake news has gained a widespread attention recently. Many existing approaches seek to fuse unimodal features to produce multimodal news representations. However, the potential of powerful cross-modal contrastive learning methods for fake news detection has not been well exploited. Besides, how to aggregate features from different modalities to boost the performance of the decision-making process is still an open question. To address that, we propose COOLANT, a cross-modal contrastive learning framework for multimodal fake news detection, aiming to achieve more accurate image-text alignment. To further capture the fine-grained alignment between vision and language, we leverage an auxiliary task to soften the loss term of negative samples during the contrast process. A cross-modal fusion module is developed to learn the cross-modality correlations. An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations. Finally, we evaluate the COOLANT and conduct a comparative study on two widely used datasets, Twitter and Weibo. The experimental results demonstrate that our COOLANT outperforms previous approaches by a large margin and achieves new state-of-the-art results on the two datasets.
Large-scale pre-trained language models have garnered significant attention in recent years due to their effectiveness in extracting sentence representations. However, most pre-trained models currently use transformer-based encoder with a single modality and are primarily designed for specific tasks such as natural language inference and question-answering. Unfortunately, this approach neglects the complementary information provided by multimodal data, which can enhance the effectiveness of sentence representation. To address this issue, we propose a Visually-supervised Pre-trained Multimodal Model (ViP) for sentence representation. Our model leverages diverse label-free multimodal proxy tasks to embed visual information into language, facilitating effective modality alignment and complementarity exploration. Additionally, our model utilizes a novel approach to distinguish highly similar negative and positive samples. We conduct comprehensive downstream experiments on natural language understanding and sentiment classification, demonstrating that ViP outperforms both existing unimodal and multimodal pre-trained models. Our contributions include a novel approach to multimodal pre-training and a state-of-the-art model for sentence representation that incorporates visual information.1 Our code is available at https://github.com/gentlefress/ViP
Vision-language models (VLMs) have shown impressive performance in substantial downstream multi-modal tasks. However, only comparing the fine-tuned performance on downstream tasks leads to the poor interpretability of VLMs, which is adverse to their future improvement. Several prior works have identified this issue and used various probing methods under a zero-shot setting to detect VLMs' limitations, but they all examine VLMs using general datasets instead of specialized ones. In practical applications, VLMs are usually applied to specific scenarios, such as e-commerce and news fields, so the generalization of VLMs in specific domains should be given more attention. In this paper, we comprehensively investigate the capabilities of popular VLMs in a specific field, the food domain. To this end, we build a food caption dataset, Food-500 Cap, which contains 24,700 food images with 494 categories. Each image is accompanied by a detailed caption, including fine-grained attributes of food, such as the ingredient, shape, and color. We also provide a culinary culture taxonomy that classifies each food category based on its geographic origin in order to better analyze the performance differences of VLM in different regions. Experiments on our proposed datasets demonstrate that popular VLMs underperform in the food domain compared with their performance in the general domain. Furthermore, our research reveals severe bias in VLMs' ability to handle food items from different geographic regions. We adopt diverse probing methods and evaluate nine VLMs belonging to different architectures to verify the aforementioned observations. We hope that our study will bring researchers' attention to VLM's limitations when applying them to the domain of food or culinary cultures, and spur further investigations to address this issue.
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment
PDF ↗Language models have been supervised with both language-only objective and visual grounding in existing studies of visual-grounded language learning. However, due to differences in the distribution and scale of visual-grounded datasets and language corpora, the language model tends to mix up the context of the tokens that occurred in the grounded data with those that do not. As a result, during representation learning, there is a mismatch between the visual information and the contextual meaning of the sentence. To overcome this limitation, we propose GroundedBERT - a grounded language learning method that enhances the BERT representation with visually grounded information. GroundedBERT comprises two components: (i) the original BERT which captures the contextual representation of words learned from the language corpora, and (ii) a visual grounding module which captures visual information learned from visual-grounded datasets. Moreover, we employ Optimal Transport (OT), specifically its partial variant, to solve the fractional alignment problem between the two modalities. Our proposed method significantly outperforms the baseline language models on various language tasks of the GLUE and SQuAD datasets.