Enhancing Person Re-Identification Performance Through In Vivo Learning

doi:10.1109/TIP.2023.3341762

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Enhancing Person Re-Identification Performance Through In Vivo Learning
	Huang, Yan 1; Huang, Yan 1; Zhang, Zhang 1; Wu, Qiang 2; Zhong, Yi 3; Wang, Liang 1
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2024
卷号	33 页码:639-654
关键词	Person re-identification in vivo learning boosting performance
ISSN号	1057-7149
DOI	10.1109/TIP.2023.3341762
通讯作者	Huang, Yan(yhuang@nlpr.ia.ac.cn) ; Wang, Liang(wangliang@nlpr.ia.ac.cn)
英文摘要	This research investigates the potential of in vivo learning to enhance visual representation learning for image-based person re-identification (re-ID). Compared to traditional self-supervised learning (which require external data), the introduced in vivo learning utilizes supervisory labels generated from pedestrian images to improve re-ID accuracy without relying on external data sources. Three carefully designed in vivo learning tasks, leveraging statistical regularities within images, are proposed without the need for laborious manual annotations. These tasks enable feature extractors to learn more comprehensive and discriminative person representations by jointly modeling various aspects of human biological structure information, contributing to enhanced re-ID performance. Notably, the method seamlessly integrates with existing re-ID frameworks, requiring minimal modifications and no additional data beyond the existing training set. Extensive experiments on diverse datasets, including Market1501, CUHK03-NP, Celeb-reID, Celeb-reid-light, PRCC, and LTCC, demonstrate substantial enhancements in rank-1 precision compared to state-of-the-art methods.
资助项目	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001140427400002
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54780]
专题	多模态人工智能系统全国重点实验室
通讯作者	Huang, Yan; Wang, Liang
作者单位	1.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Percept & Comp, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Technol Sydney UTS, Sch Elect & Data Engn, Ultimo, NSW 2007, Australia 3.Beijing Inst Technol BIT, Sch Informat & Elect, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Huang, Yan,Huang, Yan,Zhang, Zhang,et al. Enhancing Person Re-Identification Performance Through In Vivo Learning[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2024,33:639-654.
APA	Huang, Yan,Huang, Yan,Zhang, Zhang,Wu, Qiang,Zhong, Yi,&Wang, Liang.(2024).Enhancing Person Re-Identification Performance Through In Vivo Learning.IEEE TRANSACTIONS ON IMAGE PROCESSING,33,639-654.
MLA	Huang, Yan,et al."Enhancing Person Re-Identification Performance Through In Vivo Learning".IEEE TRANSACTIONS ON IMAGE PROCESSING 33(2024):639-654.