Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision最新文献

Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization. 基于频率正则化的图像重构非训练网络先验算法。

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2025-01-01 Epub Date: 2024-12-05 DOI: 10.1007/978-3-031-72630-9_20

Yilin Liu, Yunkui Pang, Jiang Li, Yong Chen, Pew-Thian Yap

{"title":"Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization.","authors":"Yilin Liu, Yunkui Pang, Jiang Li, Yong Chen, Pew-Thian Yap","doi":"10.1007/978-3-031-72630-9_20","DOIUrl":"10.1007/978-3-031-72630-9_20","url":null,"abstract":"Untrained networks inspired by deep image priors have shown promising capabilities in recovering high-quality images from noisy or partial measurements without requiring training sets. Their success is widely attributed to implicit regularization due to the spectral bias of suitable network architectures. However, the application of such network-based priors often entails superfluous architectural decisions, risks of overfitting, and lengthy optimization processes, all of which hinder their practicality. To address these challenges, we propose efficient architecture-agnostic techniques to directly modulate the spectral bias of network priors: 1) bandwidth-constrained input, 2) bandwidth-controllable upsamplers, and 3) Lipschitz-regularized convolutional layers. We show that, with just a few lines of code, we can reduce overfitting in underperforming architectures and close performance gaps with high-performing counterparts, minimizing the need for extensive architecture tuning. This makes it possible to employ a more compact model to achieve performance similar or superior to larger models while reducing runtime. Demonstrated on inpainting-like MRI reconstruction task, our results signify for the first time that architectural biases, overfitting, and runtime issues of untrained network priors can be simultaneously addressed without architectural modifications. Our code is publicly available .","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"15072 ","pages":"341-358"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11670387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data.

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2025-01-01 Epub Date: 2024-10-31 DOI: 10.1007/978-3-031-73039-9_15

Zhengfeng Lai, Joohi Chauhan, Brittany N Dugger, Chen-Nee Chuah

{"title":"Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data.","authors":"Zhengfeng Lai, Joohi Chauhan, Brittany N Dugger, Chen-Nee Chuah","doi":"10.1007/978-3-031-73039-9_15","DOIUrl":"10.1007/978-3-031-73039-9_15","url":null,"abstract":"Contrastive Language-Image Pre-training (CLIP) has shown its proficiency in acquiring distinctive visual representations and exhibiting strong generalization across diverse vision tasks. However, its effectiveness in pathology image analysis, particularly with limited labeled data, remains an ongoing area of investigation due to challenges associated with significant domain shifts and catastrophic forgetting. Thus, it is imperative to devise efficient adaptation strategies in this domain to enable scalable analysis. In this study, we introduce Path-CLIP, a framework tailored for a swift adaptation of CLIP to various pathology tasks. Firstly, we propose Residual Feature Refinement (RFR) with a dynamically adjustable ratio to effectively integrate and balance source and task-specific knowledge. Secondly, we introduce Hidden Representation Perturbation (HRP) and Dual-view Vision Contrastive (DVC) techniques to mitigate overfitting issues. Finally, we present the Doublet Multimodal Contrastive Loss (DMCL) for fine-tuning CLIP for pathology tasks. We demonstrate that Path-CLIP adeptly adapts pre-trained CLIP to downstream pathology tasks, yielding competitive results. Specifically, Path-CLIP achieves over +19% improvement in accuracy when utilizing mere 0.1% of labeled data in PCam with only 10 minutes of fine-tuning while running on a single GPU.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"15122 ","pages":"256-273"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems. 反问题中扩散模型近似后验抽样的零弹自适应。

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2025-01-01 Epub Date: 2024-11-10 DOI: 10.1007/978-3-031-73010-8_26

Yaşar Utku Alçalar, Mehmet Akçakaya

{"title":"Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems.","authors":"Yaşar Utku Alçalar, Mehmet Akçakaya","doi":"10.1007/978-3-031-73010-8_26","DOIUrl":"10.1007/978-3-031-73010-8_26","url":null,"abstract":"Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, there has been a trend in diffusion models for employing sophisticated noise schedules that involve more frequent iterations of timesteps at lower noise levels, thereby improving image generation and convergence speed. However, application of these ideas for solving inverse problems with diffusion models remain challenging, as these noise schedules do not perform well when using empirical tuning for the forward model log-likelihood term weights. To tackle these challenges, we propose zero-shot approximate posterior sampling (ZAPS) that leverages connections to zero-shot physics-driven deep learning. ZAPS fixes the number of sampling steps, and uses zero-shot training with a physics-guided loss function to learn log-likelihood weights at each irregular timestep. We apply ZAPS to the recently proposed diffusion posterior sampling method as baseline, though ZAPS can also be used with other posterior sampling diffusion models. We further approximate the Hessian of the logarithm of the prior using a diagonalization approach with learnable diagonal entries for computational efficiency. These parameters are optimized over a fixed number of epochs with a given computational budget. Our results for various noisy inverse problems, including Gaussian and motion deblurring, inpainting, and super-resolution show that ZAPS reduces inference time, provides robustness to irregular noise schedules and improves reconstruction quality. Code is available at https://github.com/ualcalar17/ZAPS.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"15141 ","pages":"444-460"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11736016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale.

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2025-01-01 Epub Date: 2024-11-08 DOI: 10.1007/978-3-031-73223-2_27

Keenon Werling, Janelle Kaneda, Tian Tan, Rishi Agarwal, Six Skov, Tom Van Wouwe, Scott Uhlrich, Nicholas Bianco, Carmichael Ong, Antoine Falisse, Shardul Sapkota, Aidan Chandra, Joshua Carter, Ezio Preatoni, Benjamin Fregly, Jennifer Hicks, Scott Delp, C Karen Liu

{"title":"AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale.","authors":"Keenon Werling, Janelle Kaneda, Tian Tan, Rishi Agarwal, Six Skov, Tom Van Wouwe, Scott Uhlrich, Nicholas Bianco, Carmichael Ong, Antoine Falisse, Shardul Sapkota, Aidan Chandra, Joshua Carter, Ezio Preatoni, Benjamin Fregly, Jennifer Hicks, Scott Delp, C Karen Liu","doi":"10.1007/978-3-031-73223-2_27","DOIUrl":"10.1007/978-3-031-73223-2_27","url":null,"abstract":"While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of movements. We present the AddBiomechanics Dataset 1.0, which includes physically accurate human dynamics of 273 human subjects, over 70 hours of motion and force plate data, totaling more than 24 million frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results. The AddBiomechanics Dataset is publicly available at addbiomechanics.org/download_data.html.","PeriodicalId":72676,"journal":{"name":"Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision","volume":"15146 ","pages":"490-508"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11948690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval 面向无监督视频检索的双流知识保持哈希算法

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2023-10-12 DOI: 10.1007/978-3-031-19781-9_11

P. Li, Hongtao Xie, Jiannan Ge, Lei Zhang, Shaobo Min, Yongdong Zhang

引用次数: 9

Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Reference Understanding 通过视角旋转和关系推理的空间和视觉视角获取对具体化参考理解的影响

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2023-09-03 DOI: 10.1007/978-3-031-20059-5_12

Cheng Shi, Sibei Yang

引用次数: 5

Rethinking Confidence Calibration for Failure Prediction 失效预测置信度校准的再思考

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2023-03-06 DOI: 10.1007/978-3-031-19806-9_30

Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu

引用次数: 11

PCR-CG: Point Cloud Registration via Deep Explicit Color and Geometry PCR-CG:点云配准通过深显色和几何

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2023-02-28 DOI: 10.1007/978-3-031-20080-9_26

Yu Zhang, Junle Yu, Xiaolin Huang, Wenhui Zhou, Ji Hou

引用次数: 7

Diverse Human Motion Prediction Guided by Multi-level Spatial-Temporal Anchors 基于多层次时空锚点的多种人体运动预测

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2023-02-09 DOI: 10.1007/978-3-031-20047-2_15

Sirui Xu, Yu-Xiong Wang, Liangyan Gui

引用次数: 15

Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection 桥接图像和视频:一个用于大词汇视频对象检测的简单学习框架

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision Pub Date : 2022-12-20 DOI: 10.1007/978-3-031-19806-9_14

Sanghyun Woo, KwanYong Park, Seoung Wug Oh, In-So Kweon, Joon-Young Lee

引用次数: 3