pwc-archive/papers-with-abstracts huggingface dataset:
DVHGNN: Multi-Scale Dilated Vision HGNN for Efficient Vision Recognition
Authors: Caoshuo Li, Tanzhe Li, Xiaobin Hu, Donghao Luo, Taisong Jin
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: graph construction, Graph Neural Network

    Recently, Vision Graph Neural Network (ViG) has gained considerable attention in computer vision. Despite its groundbreaking innovation, Vision Graph Neural Network encounters key issues including the quadratic computational complexity caused by its K-Nearest Neighbor (KNN) graph construction and the limitation of pairwise relations of normal graphs. To address the aforementioned challenges, we propose a novel vision architecture, termed Dilated Vision HyperGraph Neural Network (DVHGNN), which is designed to leverage multi-scale hypergraph to  efficiently capture high-order correlations among objects. Specifically, the proposed method tailors Clustering and Dilated HyperGraph Construction (DHGC) to adaptively capture multi-scale dependencies among the data samples. Furthermore,  a dynamic hypergraph convolution mechanism is proposed to facilitate adaptive feature exchange and fusion at the hypergraph level. Extensive qualitative and quantitative evaluations of the benchmark image datasets demonstrate that the proposed DVHGNN significantly outperforms the state-of-the-art vision backbones. For instance, our DVHGNN-S achieves an impressive top-1 accuracy of 83.1%  on ImageNet-1K, surpassing ViG-S by +1.0% and ViHGNN-S by +0.6%.
MegaSaM: Accurate, Fast and Robust Structure and Motion from Casual Dynamic Videos
Authors: Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, Noah Snavely
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Depth Estimation

    We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes. Most conventional structure from motion and monocular SLAM techniques assume input videos that feature predominantly static scenes with large amounts of parallax. Such methods tend to produce erroneous estimates in the absence of these conditions. Recent neural network based approaches attempt to overcome these challenges; however, such methods are either computationally expensive or brittle when run on dynamic videos with uncontrolled camera motion or unknown field of view. We demonstrate the surprising effectiveness of the deep visual SLAM framework, and with careful modifications to its training and inference schemes, this system can scale to real-world videos of complex dynamic scenes with unconstrained camera paths, including videos with little camera parallax. Extensive experiments on both synthetic and real videos demonstrate that our system is significantly more accurate and robust at camera pose and depth estimation when compared with prior and concurrent work, with faster or comparable running times.
Learning Conditional Space-Time Prompt Distributions for Video Class-Incremental Learning
Authors: Xiaohan Zou, Wenchao Ma, Shu Zhao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning, Prompt Learning

    Recent advancements in prompt-based learning have significantly advanced image and video class-incremental learning. However, the prompts learned by these methods often fail to capture the diverse and informative characteristics of videos, and struggle to generalize effectively to future tasks and classes. To address these challenges, this paper proposes modeling the distribution of space-time prompts conditioned on the input video using a diffusion model. This generative approach allows the proposed model to naturally handle the diverse characteristics of videos, leading to more robust prompt learning and enhanced generalization capabilities. Additionally, we develop a simple yet effective mechanism to transfer the token relationship modeling capabilities of pre-trained image transformers to spatio-temporal modeling in videos. Our approach has been thoroughly evaluated across four established benchmarks, showing remarkable improvements over existing state-of-the-art methods in video class-incremental learning.
Minimal Interaction Seperated Tuning: A New Paradigm for Visual Adaptation
Authors: Ningyuan Tang, Minghao Fu, Jianxin Wu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: CPU, GPU

    The rapid scaling of large vision pretrained models makes fine-tuning tasks more and more difficult on devices with low computational resources. We explore a new visual adaptation paradigm called separated tuning, which treats large pretrained models as standalone feature extractors that run on powerful cloud servers. The fine-tuning carries out on devices which possess only low computational resources (slow CPU, no GPU, small memory, etc.) Existing methods that are potentially suitable for our separated tuning paradigm are discussed. But, three major drawbacks hinder their application in separated tuning: low adaptation capability, large adapter network, and in particular, high information transfer overhead. To address these issues, we propose Minimal Interaction Separated Tuning, or MIST, which reveals that the sum of intermediate features from pretrained models not only has minimal information transfer but also has high adaptation capability. With a lightweight attention-based adaptor network, MIST achieves information transfer efficiency, parameter efficiency, computational and memory efficiency, and at the same time demonstrates competitive results on various visual adaptation benchmarks.
ProjAttacker: A Configurable Physical Adversarial Attack for Face Recognition via Projector
Authors: Yuanwei Liu, Hui Wei, Chengyu Jia, Ruqi Xiao, Weijian Ruan, Xingxing Wei, Joey Tianyi Zhou, Zheng Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Adversarial Attack, Face Recognition

    Previous physical adversarial attacks have shown that carefully crafted perturbations can deceive face recognition systems, revealing critical security vulnerabilities. However, these attacks often struggle to impersonate multiple targets and frequently fail to bypass liveness detection. For example, attacks using human-skin masks are challenging to fabricate, inconvenient to swap between users, and often fail liveness detection due to facial occlusions. A projector, however, can generate content-rich light without obstructing the face, making it ideal for non-intrusive attacks. Thus, we propose a novel physical adversarial attack using a projector and explore the superposition of projected and natural light to create adversarial facial images. This approach eliminates the need for physical artifacts on the face, effectively overcoming these limitations. Specifically, our proposed ProjAttacker generates adversarial 3D textures that are projected onto human faces. To ensure physical realizability, we introduce a light reflection function that models complex optical interactions between projected light and human skin, accounting for reflection and diffraction effects. Furthermore, we incorporate camera Image Signal Processing (ISP) simulation to maintain the robustness of adversarial perturbations across real-world diverse imaging conditions. Comprehensive evaluations conducted in both digital and physical scenarios validate the effectiveness of our method.
MotionPRO: Exploring the Role of Pressure in Human MoCap and Beyond
Authors: Shenghao Ren, Yi Lu, Jiayi Huang, Jiayi Zhao, He Zhang, Tao Yu, Qiu Shen, Xun Cao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Existing human Motion Capture (MoCap) methods mostly focus on the visual similarity while neglecting the physical plausibility. As a result, downstream tasks such as driving virtual human in 3D scene or humanoid robots in real world suffer from issues such as timing drift and jitter, spatial problems like sliding and penetration, and poor global trajectory accuracy. In this paper, we revisit human MoCap from the perspective of interaction between human body and physical world by exploring the role of pressure. Firstly, we construct a large-scale human Motion capture dataset with Pressure, RGB  and Optical sensors (named MotionPRO), which comprises 70 volunteers performing 400 types of motion, encompassing a total of 12.4M pose frames. Secondly, we examine both the necessity and effectiveness of the pressure signal through two challenging tasks: (1) pose and trajectory estimation based solely on pressure: We propose a network that incorporates a small kernel decoder and a long-short-term attention module, and proof that pressure could provide accurate global trajectory and plausible lower body pose.  (2) pose and trajectory estimation by fusing pressure and RGB: We impose constraints on orthographic similarity along the camera axis and whole-body contact along the vertical axis to enhance the cross-attention strategy to fuse pressure and RGB feature maps. Experiments demonstrate that fusing pressure with RGB features not only significantly improves performance in terms of objective metrics, but also plausibly drives virtual humans (SMPL) in 3D scene. Furthermore, we demonstrate that incorporating physical perception enables humanoid robots to perform more precise and stable actions, which is highly beneficial for the development of embodied artificial intelligence. Project page is available at: https://nju-cite-mocaphumanoid.github.io/MotionPRO/
D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition.
Authors: Haoran Wang, Xinji Mai, Zeng Tao, Xuan Tong, Junxiong Lin, Yan Wang, Jiawen Yu, Shaoqi Yan, Ziheng Zhou, Wenqiang Zhang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Dynamic Facial Expression Recognition, Facial Expression Recognition

    The current advancements in Dynamic Facial Expression Recognition (DFER) methods mainly focus on better capturing the spatial and temporal features of facial expressions. However, DFER datasets contain a substantial amount of noisy samples, and few have addressed the issue of handling this noise. We identified two types of noise: one is caused by low-quality data resulting from factors such as occlusion, dim lighting, and blurriness; the other arises from mislabeled data due to annotation bias by annotators. Addressing the two types of noise, we have meticulously crafted a Dynamic Dual-Stage Purification (D2SP) Framework. This initiative aims to dynamically purify the DFER datasets of these two types of noise, ensuring that only high-quality and correctly labeled data is used in the training process. To mitigate low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which computes sample weights and prunes those low-weight samples. After CGP, the Fine-Grained Correction (FGC) stage evaluates prediction stability to correct mislabeled data. Moreover, D2SP is conceived as a general and plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methods. Extensive experiments covering prevalent DFER datasets and deploying multiple benchmark methods have substantiated D2SP's ability to significantly enhance performance metrics.
Random Conditioning for Diffusion Model Compression with Distillation
Authors: Dohyun Kim, Sehwan Park, Geonhee Han, Seung Wook Kim, Paul Hongsuck Seo
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Knowledge Distillation, model, Model Compression

    Diffusion models generate high-quality images through progressive denoising but are computationally intensive due to large model sizes and repeated sampling. Knowledge distillation--transferring knowledge from a complex teacher to a simpler student model--has been widely studied in recognition tasks, particularly for transferring concepts unseen during student training. However, its application to diffusion models remains underexplored, especially in enabling student models to generate concepts not covered by the training images. In this work, we propose Random Conditioning, a novel approach that pairs noised images with randomly selected text conditions to enable efficient, image-free knowledge distillation.  By leveraging this technique, we show that the student can generate concepts unseen in the training images. When applied to conditional diffusion model distillation, our method allows the student to explore the condition space without generating condition-specific images, resulting in notable improvements in both generation quality and efficiency. This promotes resource-efficient deployment of generative diffusion models, broadening their accessibility for both research and real-world applications. Code, models, and datasets are available at: https://dohyun-as.github.io/Random-Conditioning
SOAP: Vision-Centric 3D Semantic Scene Completion with Scene-Adaptive Decoder and Occluded Region-Aware View Projection
Authors: Hyo-Jun Lee, Yeong Jun Koh, HanUl Kim, Hyunseop Kim, Yonguk Lee, Jinu Lee
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Semantic Scene Completion, Decoder

    Existing view transformations in vision-centric 3D Semantic Scene Completion (SSC) inevitably experience erroneous feature duplication in the reconstructed voxel space due to occlusions, leading to a dilution of informative contexts. Furthermore, semantic classes exhibit high variability in their appearance in real-world driving scenarios. To address these issues, we introduce a novel 3D SSC method, called SOAP, including two key components: an occluded region-aware view projection and a scene-adaptive decoder. The occluded region-aware view projection effectively converts 2D image features into voxel space, refining the duplicated features of occluded regions using information gathered from previous observations. The scene-adaptive decoder guides query embeddings to learn diverse driving environments based on a comprehensive semantic repository. Extensive experiments validate that the proposed SOAP significantly outperforms existing methods for the vision-centric 3D SSC on automated driving datasets, SemanticKITTI and SSCBench. Code is available at  https://github.com/gywns6287/SOAP.
Label Shift Meets Online Learning: Ensuring Consistent Adaptation with Universal Dynamic Regret
Authors: Yucong Dai, Shilin Gu, Ruidong Fan, Chao Xu, Chenping Hou
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Label shift, which investigates the adaptation of label distributions between the fixed source and target domains, has attracted significant research interests and broad applications in offline settings. In real-world scenarios, however, data often arrives as a continuous stream. Addressing label shift in online learning settings is paramount. Existing strategies, which tailor traditional offline label shift techniques to online settings, have degraded performance due to the inconsistent estimation of label distributions and violation of convex assumption for theoretical guarantee. In this paper, we propose a novel method to ensure consistent adaptation to online label shift. We construct a new convex risk estimator that is pivotal for both online optimization and theoretical analysis. Furthermore, we enhance an optimistic online algorithm as the base learner and refine the classifier using an ensemble method. Theoretically, we derive a universal dynamic regret which achieves minimax optimal. Extensive experiments on both real-world datasets and human motion task demonstrate the superiority of our method comparing existing methods.
Test-Time Fine-Tuning of Image Compression Models for Multi-Task Adaptability
Authors: Unki Park, Seongmoon Jeong, Youngchan Jang, Gyeong-Moon Park, Jong Hwan Ko
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Compression

    The field of computer vision was initially inspired by the human visual system and has progressively expanded to include a broader range of machine vision applications. Consequently, image compressors should be designed to effectively accommodate not only human visual perception but also machine vision tasks, including closed-set scenarios that enable pre-training and open-set scenarios that involve previously unseen tasks at test time. Many recent studies effectively address both human visual perception and closed-set machine vision tasks simultaneously but struggle to handle open-set machine vision tasks. To address this issue, this paper proposes a fully instance-specific test time fine-tuning (TTFT) for adapting learned image compression (LIC) to both closed-set and open-set machine vision tasks effectively. With our method, a large-scale LIC model, originally trained for human perception, is adapted to the target task through TTFT using Singular Value Decomposition based Low Rank Adaptation (SVD-LoRA). During TTFT, the decoder adopts a modified learning scheme that focuses exclusively on training the singular values, which helps prevent excessive bitstream overhead. This enables fully instance-specific optimization for the target task, even for open-set tasks. Experimental results demonstrate that the proposed method effectively adapts the backbone compressor to diverse machine vision tasks, outperforming competing methods.
Dual Prompting Image Restoration with Diffusion Transformers
Authors: Dehong Kong, Fan Li, Zhixin Wang, Jiaqi Xu, Renjing Pei, Wenbo Li, Wenqi Ren
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Restoration

    Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due to their limited capabilities. Diffusion transformers (DiTs), like SD3, are emerging as a promising alternative because of their better quality with scalability. However, previous conditional control methods for U-Net-based diffusion models, such as ControlNet, are not well-suited for DiTs. In this paper, we introduce DPIR (Dual Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due to their limited capabilities. Diffusion transformers (DiTs), like SD3, are emerging as a promising alternative because of their better quality with scalability. In this paper, we introduce DPIR (Dual Prompting Image Restoration), a novel image restoration method that effectivly extracts conditional information of low-quality images from multiple perspectives. Specifically, DPIR consits of two branches: a low-quality image conditioning branch and a dual prompting control branch. The first branch utilizes a lightweight module to incorporate image priors into the DiT with high efficiency. More importantly, we believe that in image restoration, textual description alone cannot fully capture its rich visual characteristics. Therefore, a dual prompting module is designed to provide DiT with additional visual cues, capturing both global context and local appearance. The extracted global-local visual prompts as extra conditional control, alongside textual prompts to form dual prompts, greatly enhance the quality of the restoration. Extensive experimental results demonstrate that DPIR delivers superior image restoration performance.
Hybrid Concept Bottleneck Models
Authors: Yang Liu, Tianwei Zhang, Shi Gu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Concept Bottleneck Models (CBMs) provide an interpretable framework for neural networks by mapping visual features to predefined, human-understandable concepts. However, the application of CBMs is often constrained by insufficient concept annotations. Recently, multi-modal pre-trained models have shown promise in reducing annotation costs by aligning visual representations with textual concept embeddings. Nevertheless, the quality and completeness of the predefined concepts significantly affect the performance of CBMs.In this work, we propose Hybrid Concept Bottleneck Model (HybridCBM), a novel CBM framework to address the challenge of incomplete predefined concepts. Our method consists of two main components: a Static Concept Bank and a Dynamic Concept Bank. The Static Concept Bank directly leverages large language models (LLMs) for concept construction, while the Dynamic Concept Bank employs learnable vectors to capture complementary and valuable concepts continuously during training. After training, a pre-trained translator converts these vectors into human-understandable concepts, further enhancing model interpretability. Notably, HybridCBM is highly flexible and can be easily applied to any CBM to improve performance. Experimental results across multiple datasets demonstrate that HybridCBM outperforms current state-of-the-art methods and achieves comparable results to black-box models. Additionally, we propose novel metrics to evaluate the quality of the learned concepts, showing that they perform comparably to predefined concepts.
UCM-VeID V2: A Richer Dataset and A Pre-training Method for UAV Cross-Modality Vehicle Re-Identification
Authors: Xingyue Liu, Jiahao Qi, Chen Chen, Kangcheng Bin, Ping Zhong
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Reconstruction, Self-Supervised Learning, Vehicle Re-Identification

    Cross-Modality Re-Identification (VI-ReID) aims to achieve around-the-clock target matching, benefiting from the strengths of both  RGB and infrared (IR) modalities. However, the field is hindered by limited datasets, particularly for vehicle VI-ReID, and by challenges such as modality bias training (MBT), stemming from biased pre-training on ImageNet. To tackle the above issues, this paper introduces an UCM-VeID V2 dataset benchmark for vehicle VI-ReID, and proposes a new self-supervised pre-training method, Cross-Modality Patch-Mixed Self-supervised Learning (PMSL). UCM-VeID V2 dataset features a significant increase in data volume, along with enhancements in multiple aspects. PMSL addresses MBT by learning modality-invariant features through Patch-Mixed Image Reconstruction (PMIR) and Modality Discrimination Adversarial Learning (MDAL), and enhances discriminability with Modality-Augmented Contrasting Cluster (MACC).  Comprehensive experiments are carried out to validate the effectiveness of the proposed method.