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Multi-level consistency regularization for domain adaptive object detection

文献类型：期刊论文

作者Tian, Kun1,2; Zhang, Chenghao1,2; Wang, Ying2; Xiang, Shiming1,2
刊名NEURAL COMPUTING & APPLICATIONS
出版日期2023-05-31
页码16
关键词Consistency regularization Object detection Domain adaptation
ISSN号0941-0643
DOI10.1007/s00521-023-08677-9
通讯作者Wang, Ying(ying.wang@ia.ac.cn)
英文摘要To improve the adaptability of detectors, most existing domain adaptation algorithms adopt adversarial learning to align feature distributions between source and target datasets. Different from previous methods, this work explores the possibility of transferring detectors with only source domain data and style information of the target domain. Specifically, we propose three consistency regularizations to enhance the adaptation performance of the detector. First, the source domain and the synthetic domain share the same image content, and the supervision regularization fully exploits the source annotations, which narrows the domain gap and saves labeling costs. Second, prediction regularization improves the robustness of the detector to category semantics and location awareness in different domains. Third, self-discovering feature regularization projects the detector's attention to object-related regions, which are more discriminative than background noise. In addition, our method can cooperate with the classic domain adaptation algorithm to further improve the generalization of the detector, which shows that both the content and style information of target domain images are crucial for the transfer process. Extensive experiments have been conducted on multiple detection benchmarks, including Foggy Cityscapes, Sim10k, KITTI, Clipart, and Watercolor datasets. The favorable performance compared with existing state-of-the-art methods confirms the effectiveness of the proposed consistency regularizations.
资助项目National Key Research and Development Program of China[2018AAA0100400] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[62076242] ; National Natural Science Foundation of China[62071466] ; National Natural Science Foundation of China[61976208]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000999183100003
出版者SPRINGER LONDON LTD
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/53387]  
专题多模态人工智能系统全国重点实验室
通讯作者Wang, Ying
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
2.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		 Tian, Kun,Zhang, Chenghao,Wang, Ying,et al. Multi-level consistency regularization for domain adaptive object detection[J]. NEURAL COMPUTING & APPLICATIONS,2023:16.
APA Tian, Kun,Zhang, Chenghao,Wang, Ying,&Xiang, Shiming.(2023).Multi-level consistency regularization for domain adaptive object detection.NEURAL COMPUTING & APPLICATIONS,16.
MLA Tian, Kun,et al."Multi-level consistency regularization for domain adaptive object detection".NEURAL COMPUTING & APPLICATIONS (2023):16.

入库方式： OAI收割

来源：自动化研究所

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