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: [email protected].
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.
