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REACT: Remainder Adaptive Compensation for Domain Adaptive Object Detection

文献类型：期刊论文

作者Li, Haochen1,2; Zhang, Rui3; Yao, Hantao4; Zhang, Xin3; Hao, Yifan3; Song, Xinkai3; Li, Ling1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2024
卷号33页码:3735-3748
关键词Unsupervised domain adaptation domain adaptive object detection feature extraction prototypes
ISSN号1057-7149
DOI10.1109/TIP.2024.3409024
通讯作者Zhang, Rui(zhangrui@ict.ac.cn) ; Li, Ling(liling@iscas.ac.cn)
英文摘要Domain adaptive object detection (DAOD) aims to infer a robust detector on the target domain with the labelled source datasets. Recent studies utilize a feature extractor shared on the source and target domains to capture the domain-invariant features and the task-relevant information with both feature-alignment constraint and source annotations. However, the feature extractor shared across domains discards partial task-relevant information of the target domain due to the domain gap and lack of target annotations, leading to compromised discrimination capabilities within target domain. To this end, we propose a novel REmainder Adaptive CompensaTion network (REACT) to adaptively compensate the extracted features with the remainder features for generating task-relevant features. The key insight is that the remainder features contain the discarded task-relevant information, so they can be adapted to compensate for the inadequate target features. Especially, REACT introduces an additional remainder branch to regain the remainder features, and then adaptively utilizes them to compensate for the discarded task-relevant information, improving discrimination on the target domain. Extensive experiments over multiple cross-domain adaptation tasks with three baselines demonstrate that our approach gains significant improvements and achieves superior performance compared with highly-optimized state-of-the-art methods.
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001248109100003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/58780]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Zhang, Rui; Li, Ling
作者单位1.Chinese Acad Sci, Inst Software, Intelligent Software Res Ctr, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Haochen,Zhang, Rui,Yao, Hantao,et al. REACT: Remainder Adaptive Compensation for Domain Adaptive Object Detection[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2024,33:3735-3748.
APA Li, Haochen.,Zhang, Rui.,Yao, Hantao.,Zhang, Xin.,Hao, Yifan.,...&Li, Ling.(2024).REACT: Remainder Adaptive Compensation for Domain Adaptive Object Detection.IEEE TRANSACTIONS ON IMAGE PROCESSING,33,3735-3748.
MLA Li, Haochen,et al."REACT: Remainder Adaptive Compensation for Domain Adaptive Object Detection".IEEE TRANSACTIONS ON IMAGE PROCESSING 33(2024):3735-3748.

入库方式： OAI收割

来源：自动化研究所

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