For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator.

Abstracts: This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning-based SIRR methods have shown significant performance degradation due to unnoticeable distortions and …

2022年10月10日 · In this paper, we propose to address the stereo image restoration with transformer by leveraging its powerful capability of modeling long-range context dependencies. Specifically, we construct a stereo image restoration transformer (SIR-Former) to effectively exploit the cross-view correlations.

The continuing COVID-19 pandemic brought us another virtual edition of the conference in 2021. The IEEE Computer Society and Computer Vision Foundation were able to jointly organize an event that attendees still found to be engaging and enjoyable.

These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work.

IEEE / CVF 计算机视觉和模式识别会议 (The IEEE / CVF Computer Vision and Pattern Recognition Conference，CVPR) 是首屈一指的一年一度计算机视觉盛会，包括主要会议和多个同地研讨会和短期课程，被认为是该领域最重要的会议之一。

2023年8月22日 · Extensive experiments on Nature, SIR 2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes. Published in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Official implementation of the paper "CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image" (CVPR 2022)

2023年2月24日 · 提出了一种新的循环多变量函数（cvf），它将其输入分解为多个子分量，并将其输出的组合重新作为输入。利用cvf设计了用于srgb图像去噪的自监督图像去噪模型（cvf-sid）。

CVPR，全称为“Conference on Computer Vision and Pattern Recognition”，即计算机视觉与模式识别会议，是计算机视觉领域中最重要的国际会议之一。 该会议汇集了来自世界各地的研究人员、学者和工程师，旨在分享最新的计算机视觉和模式识别研究成果，讨论行业趋势，以及促进领域内的合作与交流。 CVPR2022官网： Home | CVPR 2022 (thecvf.com) 社交账号：