pwc-archive/papers-with-abstracts huggingface dataset:
Cheb-GR: Rethinking K-nearest Neighbor Search in Re-ranking for Person Re-identification
Authors: Jinxi Yang, He Li, Bo Du, Mang Ye
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Person Re-Identification, Re-Ranking, Retrieval

    Person re-identification (ReID) is the task of matching individuals across different camera views. Existing approaches typically employ neural networks to extract discriminative features, ranking gallery images based on their similarities to probe images. While effective, these methods are often enhanced through re-ranking, a post-processing step that refines initial retrieval results without requiring additional model training. However, current re-ranking methods mostly rely on k-nearest neighbor search to extract similar images that might have the same identity as the query, which is time-consuming with a high computation burden, limiting their applications in reality. We rethink the effect of the k-nearest neighbor search and introduce the Chebyshev's Theorem-guided Graph Re-ranking (Cheb-GR) method, which adopts the adaptive neighbor search guided by Chebyshev's Theorem over the k-nearest neighbor search for efficient neighbor selection. Our method leverages graph convolution operations to refine image features and achieve robust re-ranking, leading to enhanced retrieval performance. Furthermore, we provide a theoretical analysis based on Chebyshev's Inequality to elucidate the factors contributing to the strong performance of the proposed method. Our method significantly reduces the computation costs while maintaining relatively strong performance. Through extensive experiments in both general and cross-domain settings, we demonstrate the effectiveness of Cheb-GR and its potential for real-world applications.
Unified Medical Lesion Segmentation via Self-referring Indicator
Authors: Shijie Chang, Xiaoqi Zhao, Lihe Zhang, Tiancheng Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: In-Context Learning, Lesion Segmentation, Segmentation

    The recently emerged in-context-learning-based (ICL-based) models have the potential towards the unification of medical lesion segmentation. However, due to their cross-fusion designs, existing ICL-based unified segmentation models fail to accurately localize lesions with low-matched reference sets. Considering that the query itself can be regarded as a high-matched reference, which better indicates the target, we design a self-referencing mechanism that adaptively extracts self-referring indicator vectors from the query based on coarse predictions, thus effectively overcoming the negative impact caused by low-match reference sets. To further facilitate the self-referring mechanism, we introduce reference indicator generation to efficiently extract reference information for coarse predictions instead of using cross-fusion modules, which heavily rely on reference sets. Our designs successfully address the challenges of applying ICL to unified medical lesion segmentation, forming a novel framework named SR-ICL. Our method achieves state-of-the-art results on 8 medical lesion segmentation tasks with only 4 image-mask pairs as reference. Notably, SR-ICL still accomplishes remarkable performance even when using weak reference annotations such as boxes and points, and maintains fixed and low memory consumption even if more tasks are combined. We hope that SR-ICL can provide new insights for the clinical application of medical lesion segmentation.
Revisiting Generative Replay for Class Incremental Object Detection
Authors: Shizhou Zhang, Xueqiang Lv, Yinghui Xing, Qirui Wu, Di Xu, Yanning Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: class-incremental learning, Class Incremental Learning, Class-Incremental Object Detection, Incremental Learning, object-detection, Object Detection

    Generative replay has gained significant attention in class-incremental learning; however, its application to Class Incremental Object Detection (CIOD) remains limited due to the challenges in generating complex images with precise spatial arrangements. In this study, motivated by the observation that the forgetting of prior knowledge is predominantly present in the classification sub-task as opposed to the localization sub-task, we revisit the generative replay method for class incremental object detection. Our method utilize a standard Stable Diffusion model to generate image-level replay data for all old and new tasks. Accordingly, the old detector and a stage-wise detector are conducted on the synthetic images respectively to determine the bounding box positions through pseudo-labeling. Furthermore, we propose to use a Similarity-based Cross Sampling mechanism to select more valuable confusing data between old and new tasks to more effectively mitigate catastrophic forgetting and reduce the false alarm rate for the new task. Finally, all synthetic and real data are integrated for current-stage detector training, where the images generated for previous tasks are highly beneficial in minimizing the forgetting of existing knowledge, while those synthesized for the new task can help bridge the domain gap between real and synthetic images. We conducted extensive experiments on PASCAL VOC 2007 and MS COCO benchmark datasets in multiple settings to showcase the efficacy of our proposed approach, which achieves state-of-the-art results. The code are available at https://github.com/qiangzai-lv/RGR-IOD.
Comprehensive Information Bottleneck for Unveiling Universal Attribution to Interpret Vision Transformers
Authors: Jung-Ho Hong, Ho-Joong Kim, Kyu-Sung Jeon, Seong-Whan Lee
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Decision Making

    The feature attribution method reveals the contribution of input variables to the decision-making process to provide an attribution map for explanation. Existing methods grounded on the information bottleneck principle compute information in a specific layer to obtain attributions, compressing the features by injecting noise via a parametric damping ratio. However, the attribution obtained in a specific layer neglects evidence of the decision-making process distributed across layers. In this paper, we introduce a comprehensive information bottleneck (CoIBA), which discovers the relevant information in each targeted layer to explain the decision-making process. Our core idea is applying information bottleneck in multiple targeted layers to estimate the comprehensive information by sharing a parametric damping ratio across the layers. Leveraging this shared ratio complements the over-compressed information to discover the omitted clues of the decision by sharing the relevant information across the targeted layers. We suggest the variational approach to fairly reflect the relevant information of each layer by upper bounding layer-wise information. Therefore, CoIBA guarantees that the discarded activation is unnecessary in every targeted layer to make a decision. The extensive experimental results demonstrate the enhancement in faithfulness of the feature attributions provided by CoIBA.
Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation
Authors: Jiao Xu, Xin Chen, Lihe Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    In this paper, we present a new dynamic collaborative network for semi-supervised 3D vessel segmentation, termed DiCo. Conventional mean teacher (MT) methods typically employ a static approach, where the roles of the teacher and student models are fixed. However, due to the complexity of 3D vessel data, the teacher model may not always outperform the student model, leading to cognitive biases that can limit performance. To address this issue, we propose a dynamic collaborative network that allows the two models to dynamically switch their teacher-student roles. Additionally, we introduce a multi-view integration module to capture various perspectives of the inputs, mirroring the way doctors conduct medical analysis. We also incorporate adversarial supervision to constrain the shape of the segmented vessels in unlabeled data. In this process, the 3D volume is projected into 2D views to mitigate the impact of label inconsistencies. Experiments demonstrate that our DiCo method sets new state-of-the-art performance on three 3D vessel segmentation benchmarks.  The code repository address is https://github.com/xujiaommcome/DiCo.
GLane3D: Detecting Lanes with Graph of 3D Keypoints
Authors: Halil İbrahim Öztürk, Muhammet Esat Kalfaoğlu, Ozsel Kilinc
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Lane Detection, Autonomous Driving, Lane Detection

    Accurate and efficient lane detection in 3D space is essential for autonomous driving systems, where robust generalization is the foremost requirement for 3D lane detection algorithms. Considering the extensive variation in lane structures worldwide, achieving high generalization capacity is particularly challenging, as algorithms must accurately identify a wide variety of lane patterns worldwide. Traditional top-down approaches rely heavily on learning lane characteristics from training datasets, often struggling with lanes exhibiting previously unseen attributes. To address this generalization limitation, we propose a method that detects keypoints of lanes and subsequently predicts sequential connections between them to construct complete 3D lanes. Each key point is essential for maintaining lane continuity, and we predict multiple proposals per keypoint by allowing adjacent grids to predict the same keypoint using an offset mechanism. PointNMS is employed to eliminate overlapping proposal keypoints, reducing redundancy in the estimated BEV graph and minimizing computational overhead from connection estimations. Our model surpasses previous state-of-the-art methods on both the Apollo and OpenLane datasets, demonstrating superior F1 scores and a strong generalization capacity when models trained on OpenLane are evaluated on the Apollo dataset, compared to prior approaches.
Cross-Rejective Open-Set SAR Image Registration
Authors: Shasha Mao, Shiming Lu, Zhaolong Du, Licheng Jiao, Shuiping Gou, Luntian Mou, Xuequan Lu, Lin Xiong, Yimeng Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Registration, Open Set Learning

    Synthetic Aperture Radar (SAR) image registration is an essential upstream task in geoscience applications, in which pre-detected keypoints from two images are employed as observed objects to seek matched-point pairs. In general, the registration is regarded as a typical closed-set classification, which forces each keypoint to be classified into the given classes, but ignoring an essential issue that numerous redundant keypoints are beyond the given classes, which unavoidably results in capturing incorrect matched-point pairs. Based on this, we propose a Cross-Rejective Open-set SAR Image Registration (CroR-OSIR) method. In this work, these redundant keypoints are regarded as out-of-distribution (OOD) samples, and we formulate the registration as a special open-set task with two modules: supervised contrastive feature-tuning and cross-rejective open-set recognition (CroR-OSR). Unlike traditional open-set recognition, all samples, including OOD samples, are available in the CroR-OSR module. CroR-OSR conducts the closed-set classifications in individual open-set domains from two images, meanwhile employing the cross-domain rejection during training, to exclude these OOD samples based on confidence and consistency. Moreover, a new supervised contrastive tuning strategy is incorporated for feature-tuning. Especially, the cross-domain estimation labels obtained by CroR-OSR are fed back to the feature-tuning module for feature-tuning, to enhance feature discriminability. The experimental results illustrate that the proposed method achieves more precise registration than the state-of-the-art methods. The code is released at https://github.com/XDyaoshi/CroR-OSIR-main.
Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
Authors: Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai, Jianyang Gu, Ziheng Zhang, Kazi Sajeed Mehrab, Elizabeth G. Campolongo, Daniel Rubenstein, Charles V. Stewart, Anuj Karpatne, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Visual Prompt Tuning

    We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bird species. Pre-trained ViTs, such as DINO, have demonstrated remarkable capabilities in extracting localized, discriminative features. However, saliency maps like Grad-CAM often fail to identify these traits, producing blurred, coarse heatmaps that highlight entire objects instead. We propose a novel approach, Prompt Class Attention Map (Prompt-CAM), to address this limitation.  Prompt-CAM learns class-specific prompts for a pre-trained ViT and uses the corresponding outputs for classification. To correctly classify an image, the true-class prompt must attend to unique image patches not present in other classes' images  (i.e., traits). As a result, the true class's multi-head attention maps reveal traits and their locations. Implementation-wise, Prompt-CAM is almost a "free lunch," requiring only a modification to the prediction head of Visual Prompt Tuning (VPT). This makes Prompt-CAM easy to train and apply, in stark contrast to other interpretable methods that require designing specific models and training processes. Extensive empirical studies on a dozen datasets from various domains (e.g., birds, fishes, insects, fungi, flowers, food, and cars) validate the superior interpretation capability of Prompt-CAM. The source code and demo are available at https://github.com/Imageomics/Prompt_CAM.
ChatHuman: Chatting about 3D Humans with Tools
Authors: Jing Lin, Yao Feng, Weiyang Liu, Michael J. Black
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Human-Object Interaction Detection, In-Context Learning, Large Language Model, Retrieval-augmented Generation

    Numerous methods have been proposed to detect, estimate, and analyze properties of people in images, including the estimation of 3D pose, shape, contact, human-object interaction, emotion, and more. While widely applicable in vision and other areas, such methods require expert knowledge to select, use, and interpret the results. To address this, we introduce ChatHuman, a language-driven system that combines and integrates the skills of these specialized methods.  ChatHuman functions as an assistant proficient in utilizing, analyzing, and interacting with tools specific to 3D human tasks, adeptly discussing and resolving related challenges. Built on a Large Language Model (LLM) framework, ChatHuman is trained to autonomously select, apply, and interpret a diverse set of tools in response to user inputs. Adapting LLMs to 3D human tasks presents challenges, including the need for domain-specific knowledge and the ability to interpret complex 3D outputs. The innovative features of ChatHuman include leveraging academic publications to instruct the LLM how to use the tools, employing a retrieval-augmented generation model to create in-context learning examples for managing new tools, and effectively discriminating and integrating tool results to enhance tasks related to 3D humans by transforming specialized 3D outputs into comprehensible formats. Our experiments demonstrate that ChatHuman surpasses existing models in both tool selection accuracy and overall performance across various 3D human tasks, and it supports interactive chatting with users. ChatHuman represents a significant step toward consolidating diverse analytical methods into a unified, robust system for 3D human tasks.
Multi-Label Prototype Visual Spatial Search for Weakly Supervised Semantic Segmentation
Authors: Songsong Duan, Xi Yang, Nannan Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

    Existing Weakly Supervised Semantic Segmentation (WSSS) relies on the CNN-based Class Activation Map (CAM) and Transformer-based self-attention map to generate class-specific masks for semantic segmentation. However, CAM and self-attention maps usually cause incomplete segmentation due to classification bias issue. To address this issue, we propose a Multi-Label Prototype Visual Spatial Search (MuP-VSS) method with a spatial query mechanism, which learns a set of learnable class token vectors as queries to search the similarity visual tokens from image patch tokens. Specifically, MuP-VSS consists of two key components: multi-label prototype representation and multi-label prototype optimization. The former designs a global embedding to learn the global tokens from the images, and then proposes a Prototype Embedding Module (PEM) to interact with patch tokens to understand the local semantic information. The latter utilizes the exclusivity and consistency principles of the multi-label prototypes to design three prototype losses to optimize them, which contain cross-class prototype (CCP) contrastive loss, cross-image prototype (CIP) contrastive loss, and patch-to-prototype (P2P) consistency loss. CCP loss models exclusivity of multi-label prototypes learned from a single image to enhance the discriminative properties of each class better. CCP loss learns the consistency of the same class-specific prototypes extracted from multiple images to enhance the semantic consistency. P2P loss is proposed to control the semantic response of the prototype to the image patches. Experimental results on Pascal VOC 2012 and MS COCO show that MuP-VSS significantly outperforms recent methods and achieves state-of-the-art performance.
SapiensID: Foundation for Human Recognition
Authors: Minchul Kim, Dingqiang Ye, Yiyang Su, Feng Liu, Xiaoming Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Face Recognition

    Existing human recognition systems often rely on separate, specialized models for face and body analysis, limiting their effectiveness in real-world scenarios where pose, visibility, and context vary widely. This paper introduces SapiensID, a unified model that bridges this gap, achieving robust performance across diverse settings. SapiensID introduces (i) Retina Patch (RP), a dynamic patch generation scheme that adapts to subject scale and ensures consistent tokenization of regions of interest; (ii) Semantic Attention Head (SAH), an attention mechanism that learns pose-invariant representations by pooling features around key body parts; and (iii) a masked recognition model (MRM) that learns from variable token length. To facilitate training, we introduce WebBody4M, a large-scale dataset capturing diverse poses and scale variations. Extensive experiments demonstrate that SapiensID achieves state-of-the-art results on various body ReID benchmarks, outperforming specialized models in both short-term and long-term scenarios while remaining competitive with dedicated face recognition systems. Furthermore, SapiensID establishes a strong baseline for the newly introduced challenge of Cross Pose-Scale ReID, demonstrating its ability to generalize to complex, real-world conditions.The dataset, code and models will be released.
Reasoning Mamba: Hypergraph-Guided Region Relation Calculating for Weakly Supervised Affordance Grounding
Authors: Yuxuan Wang, Aming Wu, Muli Yang, Yukuan Min, Yihang Zhu, Cheng Deng
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Human-Object Interaction Detection, Mamba, Relation

    This paper pays attention to Weakly Supervised Affordance Grounding (WSAG) task that aims to train model to identify affordance regions using human-object interaction images and egocentric images without the need for costly pixel-level annotations. Most existing methods usually consider the affordance regions to be isolated and directly employ class activation maps to conduct localization, ignoring the relationships with other object components and weakening the performance. For example, a cup's handle is combined with its body to achieve the pouring ability. Obviously, capturing the region relationships is beneficial for improving the localization accuracy of affordance regions. To this end, we first explore exploiting hypergraph to discover these relations and propose a Reasoning Mamba (R-Mamba) framework. We first extract feature embeddings from exo-centric and egocentric images to construct the hypergraphs consisting of multiple vertices and hyperedges, which capture the in-context local region relationships between different visual components. Subsequently, we design a Hypergraph-guided State Space (HSS) block to reorganize these local relationships from the global perspective. By this mechanism, the model could leverage the captured relationships to improve the localization accuracy of affordance regions. Extensive experiments and visualization analyses demonstrate the superiority of our method.
M3GYM: A Large-Scale Multimodal Multi-view Multi-person Pose Dataset for Fitness Activity Understanding in Real-world Settings
Authors: Qingzheng Xu, Ru Cao, Xin Shen, Heming Du, Sen Wang, Xin Yu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 2D Human Pose Estimation, 3D Human Pose Estimation, Human Mesh Recovery, Multi-view 3D Human Pose Estimation, Pose Estimation

    Human pose estimation is a critical task in computer vision for applications in sports analysis, healthcare monitoring, and human-computer interaction. However, existing human pose datasets are collected either from custom-configured laboratories with complex devices or they only include data on single individuals, and both types typically capture daily activities. In this paper, we introduce the M3GYM dataset, a large-scale multimodal, multi-view, and multi-person pose dataset collected from a real gym to address the limitations of existing datasets.Specifically, we collect videos for 82 sessions from the gym, each session lasting between 40 to 60 minutes. These videos are gathered by 8 cameras, including over 50 subjects and 47 million frames. These sessions include 51 Normal fitness exercise sessions as well as 17 Pilates and 14 Yoga sessions. The exercises cover a wide range of poses and typical fitness activities, particularly in Yoga and Pilates, featuring poses with stretches, bends, and twists, e.g., humble warrior, fire hydrants and knee hover side twists. Each session involves multiple subjects, leading to significant self-occlusion and mutual occlusion in single views.Moreover, the gym has two symmetric floor mirrors, a feature not seen in previous datasets, and seven lighting conditions. We provide frame-level multimodal annotations, including 2D&3D keypoints, subject IDs, and meshes. Additionally, M3GYM uniquely offers labels for over 500 actions along with corresponding assessments from sports experts.We benchmark a variety of state-of-the-art methods for several tasks, i.e., 2D human pose estimation, single-view and multi-view 3D human pose estimation, and human mesh recovery. To simulate real-world applications, we also conduct cross-domain experiments across Normal, Yoga, and Pilates sessions. The results show that M3GYM significantly improves model generalization in complex real-world settings.