PIT: A Plug-and-Play Image Translator for Making Off-the-Shelf Models Adapt to Corruptions
文献类型：期刊论文
| 作者 | Li, Yinqi1,2; Chang, Hong1,2; Shan, Shiguang1,2; Chen, Xilin1,2 |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 47期号:12页码:11644-11661 |
| 关键词 | Adaptation models Image recognition Computational modeling Translation Training Data models Image restoration Data augmentation Head Flowering plants Corrupted image recognition image-to-image translation generative adversarial network |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2025.3598147 |
| 英文摘要 | Visual recognition models pretrained on clean images usually do not perform well in the presence of image corruptions, such as blurring or noise, which limits their applicability in real-world scenarios. To solve this problem, existing approaches usually design complex data augmentations to train a robust model from scratch or adapt a pretrained model to corrupted scenarios. These approaches ignore the existence of the large number of deployed models in our community, causing extensive computation and storage costs for making deployed models adapted. Based on this consideration, this paper focuses on solving a practical problem of making many clean-image-pretrained models adapt to unlabeled corrupted images through one training procedure. To this end, we aim to learn a Plug-and-play Image Translator (PIT) that can be directly combined with recognition models after training. Existing approaches, such as vanilla image translation and restoration, are not proper for solving this problem, as they are mostly based on supervised training and are not recognition-oriented. To address this issue, we propose a recognition-oriented unsupervised image translation framework to make PIT produce images with indistinguishable recognition predictions from the clean ones. We verify the effectiveness of PIT on several recognition tasks and show that PIT boosts the performance of clean-image-pretrained models significantly in the presence of image corruptions. |
| 资助项目 | National Science and Technology Major Project[2021ZD0111901] ; National Natural Science Foundation of China (NSFC)[62376259] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001609561600050 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/43099]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Chang, Hong |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, State Key Lab AI Safety, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yinqi,Chang, Hong,Shan, Shiguang,et al. PIT: A Plug-and-Play Image Translator for Making Off-the-Shelf Models Adapt to Corruptions[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2025,47(12):11644-11661. |
| APA | Li, Yinqi,Chang, Hong,Shan, Shiguang,&Chen, Xilin.(2025).PIT: A Plug-and-Play Image Translator for Making Off-the-Shelf Models Adapt to Corruptions.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,47(12),11644-11661. |
| MLA | Li, Yinqi,et al."PIT: A Plug-and-Play Image Translator for Making Off-the-Shelf Models Adapt to Corruptions".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 47.12(2025):11644-11661. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。