PRDP: Person Reidentification With Dirty and Poor Data

doi:10.1109/TCYB.2021.3105970

	PRDP: Person Reidentification With Dirty and Poor Data
	Xu, Furong 1; Ma, Bingpeng 1; Chang, Hong 1,2; Shan, Shiguang 1,3,4
	2021-09-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
页码	13
摘要	In this article, we propose a novel method to simultaneously solve the data problem of dirty quality and poor quantity for person reidentification (ReID). Dirty quality refers to the wrong labels in image annotations. Poor quantity means that some identities have very few images (FewIDs). Training with these mislabeled data or FewIDs with triplet loss will lead to low generalization performance. To solve the label error problem, we propose a weighted label correction based on cross-entropy (wLCCE) strategy. Specifically, according to the influence range of the wrong labels, we first classify the mislabeled images into point label error and set label error. Then, we propose a weighted triplet loss (WTL) to correct the two label errors, respectively. To alleviate the poor quantity issue, we propose a feature simulation based on autoencoder (FSAE) method to generate some virtual samples for FewID. For the authenticity of the simulated features, we transfer the difference pattern of identities with multiple images (MultIDs) to FewIDs by training an autoencoder (AE)-based simulator. In this way, the FewIDs obtain richer expressions to distinguish from other identities. By dealing with a dirty and poor data problem, we can learn more robust ReID models using the triplet loss. We conduct extensive experiments on two public person ReID datasets: 1) Market-1501 and 2) DukeMTMC-reID, to verify the effectiveness of our approach.
关键词	Training Noise measurement Data models Task analysis Training data Predictive models Heuristic algorithms Dirty metric learning person reidentification (ReID) poor
DOI	10.1109/TCYB.2021.3105970
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFA0700800] ; Natural Science Foundation of China (NSFC)[61876171] ; Natural Science Foundation of China (NSFC)[61976203] ; Open Project Fund from the Shenzhen Institute of Artificial Intelligence and Robotics for Society[AC01202005015]
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000732360900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18010
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ma, Bingpeng
作者单位	1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
推荐引用方式 GB/T 7714	Xu, Furong,Ma, Bingpeng,Chang, Hong,et al. PRDP: Person Reidentification With Dirty and Poor Data[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:13.
APA	Xu, Furong,Ma, Bingpeng,Chang, Hong,&Shan, Shiguang.(2021).PRDP: Person Reidentification With Dirty and Poor Data.IEEE TRANSACTIONS ON CYBERNETICS,13.
MLA	Xu, Furong,et al."PRDP: Person Reidentification With Dirty and Poor Data".IEEE TRANSACTIONS ON CYBERNETICS (2021):13.