Meta Clothing Status Calibration for Long-Term Person Re-Identification.

Huang, Y; Wu, Q; Zhang, Z; Shan, C; Huang, Y; Zhong, Y; Wang, L

Meta Clothing Status Calibration for Long-Term Person Re-Identification.

Huang, Y

Wu, Q

Zhang, Z Shan, C Huang, Y

Zhong, Y Wang, L

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Image Process, 2024, 33, pp. 2334-2346
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.contributor.author	Zhang, Z
dc.contributor.author	Shan, C
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-1363-5318
dc.contributor.author	Zhong, Y
dc.contributor.author	Wang, L
dc.date.accessioned	2024-08-07T05:07:44Z
dc.date.available	2024-08-07T05:07:44Z
dc.date.issued	2024
dc.identifier.citation	IEEE Trans Image Process, 2024, 33, pp. 2334-2346
dc.identifier.issn	1057-7149
dc.identifier.issn	1941-0042
dc.identifier.uri	http://hdl.handle.net/10453/180273
dc.description.abstract	Recent studies have seen significant advancements in the field of long-term person re-identification (LT-reID) through the use of clothing-irrelevant or insensitive features. This work takes the field a step further by addressing a previously unexplored issue, the Clothing Status Distribution Shift (CSDS). CSDS refers to the differing ratios of samples with clothing changes to those without clothing changes between the training and test sets, leading to a decline in LT-reID performance. We establish a connection between the performance of LT-reID and CSDS, and argue that addressing CSDS can improve LT-reID performance. To that end, we propose a novel framework called Meta Clothing Status Calibration (MCSC), which uses meta-learning to optimize the LT-reID model. Specifically, MCSC simulates CSDS between meta-train and meta-test with meta-optimization objectives, optimizing the LT-reID model and making it robust to CSDS. This framework is designed to prevent overfitting and improve the generalization ability of the LT-reID model in the presence of CSDS. Comprehensive evaluations on seven datasets demonstrate that the proposed MCSC framework effectively handles CSDS and improves current state-of-the-art LT-reID methods on several LT-reID benchmarks.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Image Process
dc.relation.isbasedon	10.1109/TIP.2024.3374634
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4607 Graphics, augmented reality and games
dc.title	Meta Clothing Status Calibration for Long-Term Person Re-Identification.
dc.type	Journal Article
utslib.citation.volume	33
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Global Big Data Technologies Research Centre (GBDTC)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:07:30Z
pubs.publication-status	Published
pubs.volume	33

Abstract:

Recent studies have seen significant advancements in the field of long-term person re-identification (LT-reID) through the use of clothing-irrelevant or insensitive features. This work takes the field a step further by addressing a previously unexplored issue, the Clothing Status Distribution Shift (CSDS). CSDS refers to the differing ratios of samples with clothing changes to those without clothing changes between the training and test sets, leading to a decline in LT-reID performance. We establish a connection between the performance of LT-reID and CSDS, and argue that addressing CSDS can improve LT-reID performance. To that end, we propose a novel framework called Meta Clothing Status Calibration (MCSC), which uses meta-learning to optimize the LT-reID model. Specifically, MCSC simulates CSDS between meta-train and meta-test with meta-optimization objectives, optimizing the LT-reID model and making it robust to CSDS. This framework is designed to prevent overfitting and improve the generalization ability of the LT-reID model in the presence of CSDS. Comprehensive evaluations on seven datasets demonstrate that the proposed MCSC framework effectively handles CSDS and improves current state-of-the-art LT-reID methods on several LT-reID benchmarks.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180273