pwc-archive/papers-with-abstracts huggingface dataset:
DynRefer: Delving into Region-level Multimodal Tasks via Dynamic Resolution
Authors: Yuzhong Zhao, Feng Liu, Yue Liu, Mingxiang Liao, Chen Gong, Qixiang Ye, Fang Wan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute

    One important task of multimodal models is to translate referred image regions to human preferred language descriptions. Existing methods, however, ignore the resolution adaptability needs of different tasks, which hinders them to find out precise language descriptions. In this study, we propose a DynRefer approach, to pursue high-accuracy region-level referring through mimicking the resolution adaptability of human visual cognition. During training, DynRefer stochastically aligns language descriptions of multimodal tasks with images of multiple resolutions, which are constructed by nesting a set of random views around the referred region. This process essentially constructs a set of region representations, where suitable representations for specific tasks can be matched. During inference, DynRefer performs selectively multimodal referring by sampling proper region representations for tasks from the set of views based on image and task priors. This allows the visual information for referring to better match human preferences, thereby improving the representational adaptability of region-level multimodal models. Experiments show that DynRefer brings mutual improvement upon broad tasks including region-level captioning, open-vocabulary region recognition and attribute detection. Furthermore, DynRefer achieves state-of-the-art results on multiple region-level multimodal tasks using a single model. Code is available at https://github.com/callsys/DynRefer.
Unsupervised Continual Domain Shift Learning with Multi-Prototype Modeling
Authors: Haopeng Sun, Yingwei Zhang, Lumin Xu, Sheng Jin, Ping Luo, Chen Qian, Wentao Liu, Yiqiang Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Unsupervised Continual Domain Shift Learning

    In real-world applications, deep neural networks may encounter constantly changing environments, where the test data originates from continually shifting unlabeled target domains. This problem, known as Unsupervised Continual Domain Shift Learning (UCDSL), poses practical difficulties. Existing methods for UCDSL aim to learn domain-invariant representations for all target domains. However, due to the existence of adaptivity gap, the invariant representation may theoretically lead to large joint errors. To overcome the limitation, we propose a novel UCDSL method, called Multi-Prototype Modeling (MPM). Our model comprises two key components: (1) Multi-Prototype Learning (MPL) for acquiring domain-specific representations using multiple domain-specific prototypes. MPL achieves domain-specific error minimization instead of enforcing feature alignment across different domains. (2) Bi-Level Graph Enhancer (BiGE) for enhancing domain-level and category-level representations, resulting in more accurate predictions. We provide theoretical and empirical analysis to demonstrate the effectiveness of our proposed method. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets, highlighting its effectiveness and robustness in handling unsupervised continual domain shift learning. Codes will be publicly accessible.
Enhanced Visual-Semantic Interaction with Tailored Prompts for Pedestrian Attribute Recognition
Authors: Junyi Wu, Yan Huang, Min Gao, Yuzhen Niu, Yuzhong Chen, Qiang Wu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Pedestrian Attribute Recognition, Person Re-Identification, Prompt Learning

    Pedestrian attribute recognition (PAR) seeks to predict multiple semantic attributes associated with a specific pedestrian. There are two types of approaches for PAR: unimodal framework and bimodal framework. The former one is to seek a robust visual feature. However, the lack of exploiting semantic feature of linguistic modality is the main concern. The latter one utilizes prompt learning techniques to integrate linguistic data. However, static prompt templates and simple bimodal concatenation cannot to capture the extensive intra-class attribute variability and support active modalities collaboration. In this paper, we propose an Enhanced Visual-Semantic Interaction with Tailored Prompts (EVSITP) framework for PAR. We present an Image-Conditional Dual-Prompt Initialization Module (IDIM) to adaptively generate context-sensitive prompts from visual inputs. Subsequently, a Prompt Enhanced and Regularization Module (PERM) is proposed to strengthen linguistic information from IDIM. We further design a Bimodal Mutual Interaction Module (BMIM) to ensure bidirectional modalities communication.  In addition, existing PAR datasets are collected over a short period in limited scenarios, which do not align with real-world scenarios. Therefore, we annotate a long-term person re-identification dataset to create a new PAR dataset, Celeb-PAR. Experiments on several challenging PAR datasets show that our method outperforms state-of-the-art approaches.
On the Out-Of-Distribution Generalization of Large Multimodal Models
Authors: Xingxuan Zhang, Jiansheng Li, Wenjing Chu, Junjia Hai, Renzhe Xu, Yuqing Yang, Shikai Guan, Jiazheng Xu, Liping Jing, Peng Cui
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: In-Context Learning, Out-of-Distribution Generalization, Zero-shot Generalization

    We investigate the generalization boundaries of current Large Multimodal Models (LMMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks. We evaluate their zero-shot generalization across synthetic images, real-world distributional shifts, and specialized datasets like medical and molecular imagery. Empirical results indicate that LMMs struggle with generalization beyond common training domains, limiting their direct application without adaptation. To understand the cause of unreliable performance, we analyze three hypotheses: semantic misinterpretation, visual feature extraction insufficiency, and mapping deficiency. Results identify mapping deficiency as the primary hurdle. To address this problem, we show that in-context learning (ICL) can significantly enhance LMMs' generalization, opening new avenues for overcoming generalization barriers. We further explore the robustness of ICL under distribution shifts and show its vulnerability to domain shifts, label shifts, and spurious correlation shifts between in-context examples and test data.
AnyMap: Learning a General Camera Model for Structure-from-Motion with Unknown Distortion in Dynamic Scenes
Authors: Andrea Porfiri Dal Cin, Georgi Dikov, Jihong Ju, Mohsen Ghafoorian
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D geometry, Camera Calibration, Motion Estimation, Novel View Synthesis

    Current learning-based Structure-from-Motion (SfM) methods struggle with videos of dynamic scenes from wide-angle cameras. We present AnyMap, a differentiable SfM framework that jointly addresses image distortion and motion estimation. By learning a general implicit camera model without predefined parameters, AnyMap effectively handles lens distortion, estimating multi-view consistent 3D geometry, camera poses, and (un)projection functions. To resolve the ambiguity where motion estimation can compensate for undistortion errors and vice versa, we introduce a low-dimensional motion representation consisting of a set of learnable basis trajectories, interpolated to produce regularized motion estimates. Experimental results show that our method produces accurate camera poses, excels in camera calibration and image rectification, and enables high-quality novel view synthesis. Our low-dimensional motion representation effectively disentangles undistortion with motion estimation, outperforming existing methods.
Less Attention is More: Prompt Transformer for Generalized Category Discovery
Authors: Wei zhang, Baopeng Zhang, Zhu Teng, Wenxin Luo, Junnan Zou, Jianping Fan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, Self-Learning

    Generalized Category Discovery (GCD) typically relies on the pre-trained Vision Transformer (ViT) to extract features from a global receptive field, followed by contrastive learning to simultaneously classify unlabeled known classes and unknown classes without priors. Owing to the deficiency in the modeling capacity for inner-patch local information within ViT, current methods primarily focus on discriminative features at global level. This results in a model with more yet scattered attention, where neither excessive nor insufficient focus can grasp subtle differences to classify fine-grained unknown and known categories. To address this issue, we propose the AptGCD to deliver apt attention for GCD. It mimics the human brain how leveraging visual perception to refine local attention and comprehend global context by proposing a Meta Visual Prompt (MVP) and Prompt Transformer (PT). MVP is introduced into GCD for the first time, refining channel-level attention, while adaptively self-learning unique inner-patch features as prompts to achieve local visual modeling for our prompt transformer. Yet, relying solely on detailed features can lead to skewed judgments. Hence, PT harmonizes local and global representations, guiding the model's interpretation of features through broader contexts, thereby capturing more useful details with less attention. Extensive experiments on seven datasets demonstrate that AptGCD outperforms current methods, it achieves an average proportional 'New' accuracy improvement of approximately 9.2% over SOTA method on the all four fine-grained datasets, establishing a new standard in the field. The code is available at https://github.com/wendy26zhang/AptGCD.
Graph-Embedded Structure-Aware Perceptual Hashing for Neural Network Protection and Piracy Detection
Authors: Ruiheng Liu, Haozhe Chen, Boyao Zhao, Kejiang Chen, Weiming Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Graph Neural Network

    The advancement of AI technology has significantly influenced production activities, increasing the focus on copyright protection for AI models. Model perceptual hashing offers an efficient solution for retrieving the pirated models. Existing methods, such as handcrafted feature-based and dual-branch network-based perceptual hashing, have proven effective in detecting pirated models. However, these approaches often struggle to differentiate non-pirated models, leading to frequent false positives in model authentication and protection. To address this challenge, this paper proposes a structurally-aware perceptual model hashing technique that achieved reduced false positives while maintaining high true positive rates. Specifically, we introduce a method for converting the diverse neural network structures into graph structures suitable for DNN processing, then utilize a graph neural network to learn their structural features representation. Our approach integrates perceptual parameter-based model hashing, achieving robust performance with higher detection accuracy and fewer false positives. Experimental results show that the proposed method has only 3% false positive rate when detecting the non-pirated model, and the detection accuracy of pirated model reaches more than 98%.
Dual-Agent Optimization framework for Cross-Domain Few-Shot Segmentation
Authors: Zhaoyang Li, YuAn Wang, Wangkai Li, Tianzhu Zhang, Xiang Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Cross-Domain Few-Shot, Domain Adaptation, Sensitivity

    Cross-Domain Few-Shot Segmentation (CD-FSS) extends the generalization ability of Few-Shot Segmentation (FSS) beyond a single domain, enabling more practical applications. However, directly employing conventional FSS methods suffers from severe performance degradation in cross-domain settings, primarily due to feature sensitivity and support-to-query matching process sensitivity across domains. Existing methods for CD-FSS either focus on domain adaptation of features or delve into designing matching strategies for enhanced cross-domain robustness. Nonetheless, they overlook the fact that these two issues are interdependent and should be addressed jointly. In this work, we tackle these two issues within a unified framework by optimizing features in the frequency domain and enhancing the matching process in the spatial domain, working jointly to handle the deviations introduced by the domain gap. To this end, we propose a coherent Dual-Agent Optimization (DATO) framework, including a consistent mutual aggregation (CMA) and a correlation rectification strategy (CRS). In the consistent mutual aggregation module, we employ a set of agents to learn domain-invariant features across domains, and then use these features to enhance the original representations for feature adaptation. In the correlation rectification strategy, the agent-aggregated domain-invariant features serve as a bridge, transforming the support-to-query matching process into a referable feature space and reducing its domain sensitivity. Extensive experiments demonstrate the efficacy of our approach.
PLeaS - Merging Models with Permutations and Least Squares
Authors: Anshul Nasery, Jonathan Hayase, Pang Wei Koh, Sewoong Oh
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    The democratization of machine learning systems has made the process of fine-tuning accessible to practitioners, leading to a wide range of open-source models fine-tuned on specialized tasks and datasets. Recent work has proposed to merge such models to combine their functionalities. However, prior approaches are usually restricted to models that are fine-tuned from the same base model. Furthermore, the final merged model is typically required to be of the same size as the original models. In this work, we propose a new two-step algorithm to merge models---termed PLeaS---which relaxes these constraints.First, leveraging the Permutation symmetries inherent in the two models, PLeaS partially matches nodes in each layer by maximizing alignment. Next, PLeaS computes the weights of the merged model as a layer-wise Least Squares solution to minimize the approximation error between the features of the merged model and the permuted features of the original models. PLeaS allows a practitioner to merge two models sharing the same architecture into a single performant model of a desired size, even when the two original models are fine-tuned from different base models. We also demonstrate how our method can be extended to address a challenging scenario where no data is available from the fine-tuning domains. We demonstrate our method to merge ResNet and ViT models trained with shared and different label spaces, and show improvement over the state-of-the-art merging methods  of up to 15 percentage points for the same target compute while merging models trained on DomainNet and fine-grained classification tasks.
Convex Combination Star Shape Prior for Data-driven Image Semantic Segmentation
Authors: Xinyu Zhao, Jun Xie, Shengzhe Chen, Jun Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

    Multi-center star shape is a prevalent object shape feature, which has proven effective in model-based image segmentation methods. However, the shape field function induced by the multi-center star shape is non-smooth, and directly applying it to the data-driven image segmentation network architecture design may lead to instability in backpropagation. This paper proposes a convex combination star (CCS) shape, possessing multi-center star shape properties, and has the advantage of effectively controlling the shape of the region through a smooth field function. The sufficient condition of the proposed CCS shape can be combined into the image segmentation neural network structure design through the bridge between the variational segmentation model and the activation function of the data-driven method. Taking Segment Anything Model (SAM) and its improved version as backbone networks, we have shown that the segmentation network architecture with CCS shape properties can greatly improve the accuracy of segmentation results.
Be More Specific: Evaluating Object-centric Realism in Synthetic Images
Authors: Anqi Liang, Ciprian Corneanu, Qianli Feng, Giorgio Giannone, Aleix Martinez
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Object

    Evaluation of synthetic images is important for both model development and selection. An ideal evaluation should be specific, accurate and aligned with human perception. This paper addresses the problem of evaluating realism of objects in synthetic images. Although methods has been proposed to evaluate holistic realism, there are no methods tailored towards object-centric realism evaluation. In this work, we define a new standard for assessing object-centric realism that follows a shape-texture breakdown and proposes the first object-centric realism evaluation dataset for synthetic images. The dataset contains images generated from state-of-the-art image generative models and is richly annotated at object level across a diverse set of object categories. We then design and train the OLIP model, a dedicated architecture that considerably outperforms any existing baseline on object-centric realism evaluation.
Automatic Spectral Calibration of Hyperspectral Images: Method, Dataset and Benchmark
Authors: Zhuoran Du, ShaoDi You, Cheng Cheng, Shikui Wei
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Hyperspectral images (HSI) densely sample the world in both the space and frequency domains and, therefore, are more distinctive than RGB images. Usually, HSI needs to be calibrated to minimize the impact of various illumination conditions. The traditional way to calibrate HSI utilizes a physical reference, which involves manual operations, occlusions, and/or limits camera mobility. These limitations inspire this paper to automatically calibrate HSIs using a learning-based method. Towards this goal, a large-scale HSI calibration dataset, which has 765 high-quality HSI pairs covering diversified natural scenes and illuminations, is created. The dataset is further expanded to 7650 pairs by combining with 10 different physically measured illuminations. A spectral illumination transformer (SIT) together with an illumination attention module is proposed. Extensive benchmarks demonstrate the SoTA performance of the proposed SIT. The benchmarks also indicate that low-light conditions are more challenging than normal conditions. The dataset and codes are available online: https://github.com/duranze/Automatic-spectral-calibration-of-HSI.
Bridging Viewpoint Gaps: Geometric Reasoning Boosts Semantic Correspondence
Authors: Qiyang Qian, Hansheng Chen, Masayoshi Tomizuka, Kurt Keutzer, Qianqian Wang, Chenfeng Xu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Geometric Matching, Semantic correspondence

    Finding semantic correspondences between images is a challenging problem in computer vision, particularly under significant viewpoint changes. Previous methods rely on semantic features from pre-trained 2D models like Stable Diffusion and DINOv2, which often struggle to extract viewpoint-invariant features. To overcome this, we propose a novel approach that integrates geometric and semantic reasoning. Unlike prior methods relying on heuristic geometric enhancements, our framework fine-tunes DUSt3R on synthetic cross-instance data to reconstruct distinct objects in an aligned 3D space. By learning to deform these objects into similar shapes using semantic supervision, we enable efficient KNN-based geometric matching, followed by sparse semantic matching within local KNN candidates. While trained on synthetic data, our method generalizes effectively to real-world images, achieving up to 7.4-point improvements in zero-shot settings on the rigid-body subset of Spair-71K and up to 19.6-point gains under extreme viewpoint variations. Additionally, it accelerates runtime by up to 40 times, demonstrating both its robustness to viewpoint changes and its efficiency for practical applications.
TSAM: Temporal SAM Augmented with Multimodal Prompts for Referring Audio-Visual Segmentation
Authors: Abduljalil Radman, Jorma Laaksonen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Referring Audio-Visual Segmentation

    Referring audio-visual segmentation (Ref-AVS) aims to segment objects within audio-visual scenes using multimodal cues embedded in text expressions. While the Segment Anything Model (SAM) has revolutionized visual segmentation, its applicability to Ref-AVS, where multimodal cues act as novel prompts, remains unexplored. SAM's limitation to single-frame segmentation also hinders its ability to capture essential temporal context needed for multi-frame audio-visual segmentation. To address this gap, we propose TSAM, a novel extension of SAM designed to leverage multimodal cues for precise segmentation in dynamic audio-visual scenes. TSAM enhances SAM's image encoder with a temporal modeling branch, enabling spatio-temporal learning and deep multimodal fusion across video frames, while retaining SAM's pre-trained knowledge. Additionally, TSAM replaces SAM's user-interactive prompting mechanism with sparse and dense data-driven prompts, enabling more effective integration of audio-visual inputs and reference text expressions. Extensive experiments on the Ref-AVS dataset demonstrate TSAM's superiority over state-of-the-art methods. The results illustrate its effectiveness in segmenting objects in dynamic audio-visual scenes using text-based multimodal cues and its strong generalization to unseen objects.
High-quality Point Cloud Oriented Normal Estimation via Hybrid Angular and Euclidean Distance Encoding
Authors: Yuanqi Li, Jingcheng Huang, Hongshen Wang, Peiyuan Lv, Yansong Liu, Jiuming Zheng, Jie Guo, Yanwen Guo
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction

    The proliferation of Light Detection and Ranging (LiDAR) technology has facilitated the acquisition of three-dimensional point clouds, which are integral to applications in VR, AR, and Digital Twin. Oriented normals, critical for 3D reconstruction and scene analysis, cannot be directly extracted from scenes using LiDAR due to its operational principles. Previous traditional or learning-based methods are prone to inaccuracies due to uneven distribution and noise due to the dependence on local geometry features. This paper addresses the challenge of estimating oriented point normals by introducing a point cloud normal estimation framework via hybrid angular and Euclidean distance encoding (HAE). Our method overcomes the limitations of local geometric information by combining angular and Euclidean spaces to extract features from both point cloud coordinates and light rays, leading to more accurate normal estimation. The core of our network consists of an angular distance encoding module, which leverages both ray directions and point coordinates for unoriented normal refinement, and a ray feature fusion module for normal orientation, that is robust to noise. We also provide a point cloud dataset with ground truth normals, generated a virtual scanner, which reflects real scanning distributions and noise profiles.