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Dual Instance-Consistent Network for Cross-Domain Object Detection

文献类型：期刊论文

作者Jiao, Yifan3; Yao, Hantao4; Xu, Changsheng1,2,5
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2023-06-01
卷号45期号:6页码:7338-7352
关键词Feature extraction Object detection Detectors Visualization Proposals Head Task analysis Cross-domain object detection domain-specific description dual instance-consistent network
ISSN号0162-8828
DOI10.1109/TPAMI.2022.3218569
通讯作者Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要Cross-domain object detection aims to transfer knowledge from a labeled dataset to an unlabeled dataset. Most existing methods apply a unified embedding model to generate the tightly coupled source and target descriptions for domain alignment, leading to the destroyed feature distribution of the target domain because the embedding model is mainly controlled by the source domain. To reduce the representation bias of the target domain, we apply two independent networks to extract two types of discriminative descriptions with mutual consistency, i.e., a novel Dual Instance-Consistent Network (DICN) is proposed for cross-domain object detection. Especially, Dual Instance-Consistent Module containing the instance mutual consistency between Primary Network and Auxiliary Network is applied to align two domains, where Primary and Auxiliary Networks are used to obtain the source-specific and target-specific information, respectively. The instance mutual consistency consists of two terms: feature consistency and detection consistency, which is applied to align the instance feature and the output of detection head, respectively. With the instance mutual consistency, optimizing the Primary (Auxiliary) Network only with source (target) images by fixing the Auxiliary (Primary) Network can generate the source(target)-specific description. Extensive experiments on several benchmarks demonstrate the effectiveness of the proposed DICN, e.g., obtaining mAP of 44.10% for Cityscapes-> Foggy Cityscapes, AP on car of 76.50% for Cityscapes-> KITTI, MR (2) of 8.87%, 12.66%, 22.27%, and 42.06% for COCOPersons-> Caltech, CityPersons-> Caltech, COCOPersons-> CityPersons, and Caltech-> CityPersons, respectively.
WOS关键词ALIGNMENT
资助项目National Key Research and Development Program of China[2020AAA0106200] ; National Natural Science Foundation of China[61902399] ; National Natural Science Foundation of China[U21B2044] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[62002355] ; National Natural Science Foundation of China[61936005] ; Beijing Natural Science Foundation[L201001] ; Beijing Natural Science Foundation[4222039]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000982475600049
出版者IEEE COMPUTER SOC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/53519]  
专题多模态人工智能系统全国重点实验室
通讯作者Xu, Changsheng
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Peng Cheng Lab, Shenzhen 518055, Peoples R China
3.Nanjing Univ Posts & Telecommun, Sch Commun & Informat Engn, Nanjing, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automation, Natl Lab Pattern Recognit, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Jiao, Yifan,Yao, Hantao,Xu, Changsheng. Dual Instance-Consistent Network for Cross-Domain Object Detection[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(6):7338-7352.
APA Jiao, Yifan,Yao, Hantao,&Xu, Changsheng.(2023).Dual Instance-Consistent Network for Cross-Domain Object Detection.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(6),7338-7352.
MLA Jiao, Yifan,et al."Dual Instance-Consistent Network for Cross-Domain Object Detection".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.6(2023):7338-7352.

入库方式： OAI收割

来源：自动化研究所

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