Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Tian, Qinga; b; c; * | Cheng, Yaoa
Affiliations: [a] School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China | [b] Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China | [c] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
Correspondence: [*] Corresponding author: Qing Tian, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China. E-mail: tianqing@nuist.edu.cn.
Abstract: The aim of unsupervised domain adaptation (UDA) in person re-identification (re-ID) is to develop a model that can identify the same individual across different cameras in the target domain, using labeled data from the source domain and unlabeled data from the target domain. However, existing UDA person re-ID methods typically assume a single source domain and a single target domain, and seldom consider the scenario of multiple source domains and a single target domain. In the latter scenario, differences in sample size between domains can lead to biased training of the model. To address this, we propose an unsupervised multi-source domain adaptation person re-ID method via sample weighting. Our approach utilizes multiple source domains to leverage valuable label information and balances the inter-domain sample imbalance through sample weighting. We also employ an adversarial learning method to align the domains. The experimental results, conducted on four datasets, demonstrate the effectiveness of our proposed method.
Keywords: Person re-identification, unsupervised domain adaptation, sample weighting, unsupervised multi-source domain adaptation
DOI: 10.3233/IDA-230178
Journal: Intelligent Data Analysis, vol. 28, no. 4, pp. 943-960, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl