pwc-archive/papers-with-abstracts huggingface dataset:
Harnessing Global-Local Collaborative Adversarial Perturbation for Anti-Customization
Authors: Long Xu, Jiakai Wang, Haojie Hao, Haotong Qin, Jiejie Zhao, Xianglong Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Image Generation

    Though achieving significant success in personalized image synthesis, Latent Diffusion Models (LDMs) pose substantial social risks caused by unauthorized misuse (e.g., face theft). To counter these threats, the Anti-Customization (AC) method that exploits adversarial perturbations was proposed. Unfortunately, existing AC methods show insufficient defense ability due to the ignorance of hierarchical characteristics, i.e., global feature correlations and local facial attributes, leading to weak resistance to concept transfer and semantic theft in customization methods. To address these limitations, we are motivated to propose a **G**lobal-L**o**cal c**o**llaborate**d** Anti-Customization (GoodAC) framework to generate powerful adversarial perturbations by disturbing both feature correlations and facial attributes. To enhance the ability to resist concept transfer, we disrupt the spatial correlation of perceptual features that form the basis of model generation at a global level, thereby creating highly concept-transfer-resistant adversarial camouflage. To improve the ability to resist semantic theft, leveraging the fact that facial attributes are personalized, we designed a personalized and precise facial attribute distortion strategy locally, focusing the attack on the individual's image structure to generate strong camouflage. Extensive experiments on various customization methods, including Dreambooth and LoRA, have strongly demonstrated that our GoodAC outperforms other state-of-the-art approaches by large margins, e.g., over 50% improvements on ISM.
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
Authors: Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal, Balint Kovacs, Saikat Roy, Constantin Ulrich, Tassilo Wald, Lukas T. Rotkopf, Heinz-Peter Schlemmer, Klaus Maier-Hein
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Lesion Segmentation, Segmentation, Tumor Segmentation

    In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capable of 4D tracking with dense spatial prompts. Our model leverages an extensive dataset of 23,262 annotated medical scans, as well as synthesized longitudinal data across diverse lesion types. The diversity and scale of our dataset significantly enhances model generalizability to real-world medical imaging challenges and addresses key limitations in longitudinal data availability. LesionLocator outperforms all existing promptable models in lesion segmentation by nearly 10 dice points, reaching human-level performance, and achieves state-of-the-art results in lesion tracking, with superior lesion retrieval and segmentation accuracy. LesionLocator not only sets a new benchmark in universal promptable lesion segmentation and automated longitudinal lesion tracking but also provides the first open-access solution of its kind, releasing our synthetic 4D dataset and model to the community, empowering future advancements in medical imaging. Code is available at: www.github.com/MIC-DKFZ/LesionLocator
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Large Model Enhancement
Authors: Qianhan Feng, Wenshuo Li, Tong Lin, Xinghao Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: cross-modal alignment, Knowledge Distillation

    Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capable aritificial intelligence on mobile devices also arises, such as the AI assistant software. Some efforts try to migrate VLMs to edge devices to expand their application scope. Simplifying the model structure is a common method, but as the model shrinks, the trade-off between performance and size becomes more and more difficult. Knowledge distillation (KD) can help models improve comprehensive capabilities without increasing size or data volume. However, most of the existing large model distillation techniques only consider applications on single-modal LLMs, or only use teachers to create new data environments for students. None of these methods take into account the distillation of the most important cross-modal alignment knowledge in VLMs. We propose a method called Align-KD to guide the student model to learn the cross-modal matching that occurs at the shallow layer. The teacher also helps student learn the projection of vision token into text embedding space based on the focus of text. Under the guidance of Align-KD, the 1.7B MobileVLM V2 model can learn rich knowledge from the 7B teacher model with light design of training loss, and achieve an average score improvement of 2.0 across 6 benchmarks under two training subsets respectively. Code is available at: https://github.com/fqhank/Align-KD.
Hand-held Object Reconstruction from RGB Video with Dynamic Interaction
Authors: Shijian Jiang, Qi Ye, Rengan Xie, Yuchi Huo, Jiming Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Generation, 3D geometry, Object, Object Reconstruction, Pose Estimation

    This work aims to reconstruct the 3D geometry of a rigid object manipulated by one or both hands using monocular RGB video. Previous methods rely on Structure-from-Motion or hand priors to estimate relative motion between the object and camera, which typically assume textured objects or single-hand interactions. To accurately recover object geometry in dynamic interactions, we incorporate priors from 3D generation model into object pose estimation and propose semantic consistency constraints to solve the challenge of shape and texture discrepancy between the generated priors and observations. The poses are initialized, followed by joint optimization of the object poses and implicit neural representation. During optimization, a novel pose outlier voting strategy with inter-view consistency is proposed to correct large pose errors. Experiments on three datasets demonstrate that our method significantly outperforms the state-of-the-art in reconstruction quality for both single- and two-hand scenarios. Our project page: https://east-j.github.io/dynhor/
Unsupervised Discovery of Facial Landmarks and Head Pose
Authors: Satyajit Tourani, Siddharth Tourani, Arif Mahmood, Muhammad Haris Khan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Emotion Recognition, Head Pose Estimation, Landmark Tracking, Pose Estimation, Unsupervised Landmark Detection

    Unsupervised landmark and head pose estimation is fundamental in fields like biometrics, augmented reality, and emotion recognition, offering accurate spatial data without relying on labeled datasets. It enhances scalability, adaptability, and generalization across diverse settings, where manual labeling is costly. In this work we exploit Stable Diffusion to approach the challenging problem of unsupervised landmarks and head pose estimation and make following contributions. (a) We propose a semantic-aware landmark localization algorithm including a consistent landmarks selection technique. (b) To encode landmarks and their holistic configuration, we propose learning image-aware textual embedding. (c) A novel algorithm for landmarks-guided 3D head pose estimation is also proposed. (d) We refine the landmarks using head pose by innovating a 3D rendering based augmentation and pose-based batching technique while the refined landmarks, consequently improving the head pose. (e) We  report a new state-of-the-art in unsupervised facial landmark estimation across five challenging datasets including AFLW2000, MAFL, Cat-Heads, LS3D and a facial landmark tracking benchmark 300VW. In unsupervised head pose estimation, we outperform existing methods on BIWI and AFLW2000 by visible margins. Moreover, our method provides a significant training speed-up over the existing best unsupervised landmark detection method.
SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation
Authors: Hao Du, Bo Wu, Yan Lu, Zhendong Mao
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Benchmarking, Diagnostic, Question Answering

    Vision-language temporal alignment is a crucial capability for human dynamic recognition and cognition in real-world scenarios. While existing research focuses on capturing vision-language relevance, it faces limitations due to biased temporal distributions, imprecise annotations, and insufficient compositionally. To achieve fair evaluation and comprehensive exploration, our objective is to investigate and evaluate the ability of models to achieve alignment from a temporal perspective, specifically focusing on their capacity to synchronize visual scenarios with linguistic context in a temporally coherent manner. As a preliminary step, we present the statistical analysis of existing benchmarks and reveal the existing challenges from a decomposed perspective. To this end, we introduce SVLTA, the synthetic vision-language temporal alignment derived via a well-designed and feasible control generation method within a simulation environment. The approach considers commonsense knowledge, manipulable action, and constrained filtering, which generates reasonable, diverse, and balanced data distributions for diagnostic evaluations. Our experiments reveal diagnostic insights through the evaluations in temporal question answering, distributional shift sensitiveness, and temporal alignment adaptation.
Boosting Point-Supervised Temporal Action Localization through Integrating Query Reformation and Optimal Transport
Authors: Mengnan Liu, Le Wang, Sanping Zhou, Kun Xia, Xiaolong Sun, Gang Hua
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Localization, Multiple Instance Learning, Temporal Action Localization

    Point-supervised Temporal Action Localization poses significant challenges due to the difficulty of identifying complete actions with a single-point annotation per action.  Existing methods typically employ Multiple Instance Learning, which struggles to capture global temporal context and requires heuristic post-processing. In research on fully-supervised tasks, DETR-based structures have effectively addressed these limitations. However, it is nontrivial to merely adapt DETR to this task, encountering two major bottlenecks. (1) How to integrate point label information into the model and (2) How to select optimal decoder proposals for training in the absence of complete action segment annotations. To address this issue, we introduce an end-to-end framework by integrating Query Reformation and Optimal Transport (QROT). Specifically, we encode point labels through a set of semantic consensus queries, enabling effective focus on action-relevant snippets. Furthermore, we integrate an optimal transport mechanism to generate high-quality pseudo labels. These pseudo-labels facilitate precise proposals selection based on Hungarian algorithm, significantly enhancing localization accuracy in point-supervised settings.  Extensive experiments on the THUMOS14 and ActivityNet-v1.3 datasets demonstrate that our method outperforms existing MIL-based approaches, offering more stable and accurate temporal action localization in point-level supervision.
LOD-GS: Achieving Levels of Detail using Scalable Gaussian Soup
Authors: Jianxiong Shen, Yue Qian, Xiaohang Zhan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Management

    Current 3D Gaussian Splatting methods often overlook structured information, leading to disorganized 3D Gaussians that compromise memory and structure efficiency, particularly in Level-of-Detail (LOD) management. This inefficiency results in excessive memory use, limiting their application in resource-sensitive environments like virtual reality and interactive simulations. To overcome these challenges, we introduce a scalable Gaussian Soup that enables high-performance LOD management with progressively reduced memory usage. Our method utilizes triangle primitives with Gaussian splats embedded and adaptive pruning/growing strategies to ensure high-quality scene rendering with significantly reduced memory demands. By embedding neural Gaussians within the triangle primitives through the triangle-MLP, we achieve further memory savings while maintaining rendering fidelity. Experimental results demonstrate that our approach achieves consistently superior performance than recent leading techniques across various LOD while progressively reducing memory usage.
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution
Authors: Ze-Yu Mi, Yu-Bin Yang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Data Augmentation, Image Super-Resolution, Super-Resolution

    Data augmentation (DA) stands out as a powerful technique to enhance the generalization capabilities of deep neural networks across diverse tasks. However, in low-level vision tasks, DA remains rudimentary (i.e., vanilla DA), facing a critical bottleneck due to information loss. In this paper, we introduce a novel Calibrated Attribution Maps (CAM) to generate saliency masks, followed by two saliency-based DA methods-- Attribution-Driven Data augmentation (ADD) and ADD+--designed to address this issue. CAM leverages integrated gradients and incorporates two key innovations: a global feature detector and calibrated integrated gradients. Based on CAM and the proposed methods, we have two new insights for low-level vision tasks: (1) increasing pixel diversity, as seen in vanilla DA, can improve performance, and (2) focusing on salient features while minimizing the impact of irrelevant pixels, as seen in saliency-based DA, more effectively enhances model performance. Additionally, we find and highlight the key guiding principle for designing saliency-based DA: a wider spectrum of degradation patterns. Extensive experiments demonstrate the compatibility and consistency of our method, as well as the significant performance improvement across various SR tasks and networks.
Taming Video Diffusion Prior with Scene-Grounding Guidance for 3D Gaussian Splatting from Sparse Inputs
Authors: Yingji Zhong, Zhihao LI, Dave Zhenyu Chen, Lanqing Hong, Dan Xu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, Novel View Synthesis

    Despite recent successes in novel view synthesis using 3D Gaussian Splatting (3DGS), modeling scenes with sparse inputs remains a challenge. In this work, we address two critical yet overlooked issues in real-world sparse-input modeling: extrapolation and occlusion. To tackle these issues, we propose to use a reconstruction by generation pipeline that leverages learned priors from video diffusion models to provide plausible interpretations for regions outside the field of view or occluded. However, the generated sequences exhibit inconsistencies that do not fully benefit subsequent 3DGS modeling. To address the challenge of inconsistency, we introduce a novel scene-grounding guidance based on rendered sequences from an optimized 3DGS, which tames the diffusion model to generate consistent sequences. This guidance is training-free and does not require any fine-tuning of the diffusion model. To facilitate holistic scene modeling, we also propose a trajectory initialization method. It effectively identifies regions that are outside the field of view and occluded. We further design a scheme tailored for 3DGS optimization with generated sequences. Experiments demonstrate that our method significantly improves upon the baseline and achieves state-of-the-art performance on challenging benchmarks.
MIRE: Matched Implicit Neural Representations
Authors: Dhananjaya Jayasundara, Heng Zhao, Demetrio Labate, Vishal M. Patel
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Shape Representation, Dictionary Learning, Edge Detection, Image Inpainting, Novel View Synthesis, Super-Resolution

    Implicit Neural Representations (INRs) are continuous function learners for conventional digital signal representations. With the aid of positional embeddings and/or exhaustively fine-tuned activation functions, INRs have surpassed many limitations of traditional discrete representations. However, existing works only find a continuous representation for the digital signal by solely using a single, fixed activation function throughout the INR, and it has not yet been explored to match the INR to the given signal. As current INRs are not matched to the signal being represented by the INR, we hypothesize that this approach could restrict the representation power and generalization capabilities of INRs, limiting their broader applicability. A way to match the INR to the signal being represented is through matching the activation of each layer in the sense of minimizing the mean squared loss. In this paper, we introduce MIRE, a method to find the highly matched activation function for each layer in INR through dictionary learning. To showcase the effectiveness of the proposed method, we utilize a dictionary that includes seven activation atoms: Raised Cosines (RC), Root Raised Cosines (RRC), Prolate Spheroidal Wave Function (PSWF), Sinc, Gabor Wavelet, Gaussian, and Sinusoidal. Experimental results demonstrate that MIRE not only significantly improves INR performance across various tasks, such as image representation, image inpainting, 3D shape representation, novel view synthesis, super-resolution, and reliable edge detection, but also eliminates the need for the previously required exhaustive search for activation parameters, which had to be conducted even before INR training could begin.
Building Vision Models upon Heat Conduction
Authors: Zhaozhi Wang, Yue Liu, Yunjie Tian, Yunfan Liu, YaoWei Wang, Qixiang Ye
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: GPU

    Visual representation models leveraging attention mechanisms are challenged by significant computational overhead, particularly when pursuing large receptive fields. In this study, we aim to mitigate this challenge by introducing the Heat Conduction Operator (HCO) built upon the physical heat conduction principle. HCO conceptualizes image patches as heat sources and models their correlations through adaptive thermal energy diffusion, enabling robust visual representations. HCO enjoys a computational complexity of O(N^1.5), as it can be implemented using discrete cosine transformation (DCT) operations. HCO is plug-and-play, combining with deep learning backbones produces visual representation models (termed vHeat) with global receptive fields. Experiments across vision tasks demonstrate that, beyond the stronger performance, vHeat achieves up to a 3x throughput, 80% less GPU memory allocation, and 35% fewer computational FLOPs compared to the Swin-Transformer.
Semantic and Sequential Alignment for Referring Video Object Segmentation
Authors: Feiyu Pan, Hao Fang, Fangkai Li, Yanyu Xu, Yawei Li, Luca Benini, Xiankai Lu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Instance Segmentation, Referring Video Object Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

    Referring video object segmentation (RVOS) seeks to segment the objects within a video referred by linguistic expressions. Existing RVOS solutions follow a "fuse then select" paradigm: establishing semantic correlation between visual and linguistic feature, and performing frame-level query interaction to select the instance mask per frame with instance segmentation module. This paradigm overlooks the challenge of semantic gap between the linguistic descriptor and the video object as well as the underlying clutters in the video. This paper proposes a novel Semantic and Sequential Alignment (SSA) paradigm to handle these challenges. We first insert a lightweight adapter after the vision language model (VLM) to perform the semantic alignment. Then, prior to selecting mask per frame, we exploit the trajectory-to-instance enhancement for each frame via sequential alignment. This paradigm leverages the visual-language alignment inherent in VLM during adaptation and tries to capture global information by ensembling trajectories. This helps understand videos and the corresponding descriptors by mitigating the discrepancy with intricate activity semantics, particularly when facing occlusion or similar interference. SSA demonstrates competitive performance while maintaining fewer learnable parameters.
UNICL-SAM: Uncertainty-Driven In-Context Segmentation with Part Prototype Discovery
Authors: Dianmo Sheng, Dongdong Chen, Zhentao Tan, Qiankun Liu, Qi Chu, Tao Gong, Bin Liu, Jing Han, Wenbin Tu, Shengwei Xu, Nenghai Yu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

    Recent advancements in in-context segmentation generalists have demonstrated significant success in performing various image segmentation tasks using a limited number of labeled example images. However, real-world applications present challenges due to the variability of support examples, which often exhibit quality issues resulting from various sources and inaccurate labeling. How to extract more robust representations from these examples has always been one of the goals of in-context visual learning. In response, we propose UNICL-SAM, to better model the example distribution and extract robust representations to help in-context segmentation. We incorporate an uncertainty probabilistic module to quantify each example's reliability during both the training and testing phases. Utilizing this uncertainty estimation, we introduce an uncertainty-guided graph augmentation and feature refinement strategy, aimed at mitigating the impact of high-uncertainty regions to enhance the learning of robust representations. Subsequently, we construct prototypes for each example by aggregating part information, thereby creating reliable in-context instruction that effectively represents fine-grained local semantics. This approach serves as a valuable complement to traditional global pooling features. Experimental results demonstrate the effectiveness of the proposed framework, underscoring its potential for real-world applications.
BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering
Authors: Minye Wu, Haizhao Dai, Kaixin Yao, Tinne Tuytelaars, Jingyi Yu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, 3D Reconstruction, Inverse Rendering, Representation Learning, Vector Graphics

    Differentiable rendering enables efficient optimization by allowing gradients to be computed through the rendering process, facilitating 3D reconstruction, inverse rendering and neural scene representation learning. To ensure differentiability, existing solutions approximate or re-formulate traditional rendering operations using smooth, probabilistic proxies such as volumes or Gaussian primitives. Consequently, they struggle to preserve sharp edges due to the lack of explicit boundary definitions. We present a novel hybrid representation, Bezier Gaussian Triangle (BG-Triangle), that combines Bezier triangle-based vector graphics primitives with Gaussian-based probabilistic models, to maintain accurate shape modeling while conducting resolution-independent differentiable rendering. We present a robust and effective discontinuity-aware rendering technique to reduce uncertainties at object boundaries. We also employ an adaptive densification and pruning scheme for efficient training while reliably handling level-of-detail (LoD) variations. Experiments show that BG-Triangle achieves comparable rendering quality as 3DGS but with superior boundary preservation. More importantly, BG-Triangle uses a much smaller number of primitives than its alternatives, showcasing the benefits of vectorized graphics primitives and the potential to bridge the gap between classic and emerging representations.