Unsupervised domain adaptation in homogeneous distance space for person re-identification

Abstract

Data distribution alignment and clustering-based self-training are two feasible solutions to tackle unsupervised domain adaptation (UDA) on person re-identification (re-ID). Most existing alignment-based methods solely learn the source domain decision boundaries and align the data distribution of the target domain to the source domain, thus the re-ID performance on the target domain completely depends on the shared decision boundaries and how well the alignment is performed. However, two domains can hardly be precisely aligned because of the label space discrepancy of two domains, resulting in poor target domain re-ID performance. Although clustering-based self-training approaches could learn independent decision boundaries on the pseudo-labelled target domain data, they ignore both the accurate ID-related information of the labelled source domain data and the underlying relations between two domains. To fully exploit the source domain data to learn discriminative target domain ID-related features, in this paper, we propose a novel cross-domain alignment method in the homogeneous distance space, which is constructed by the newly designed stair-stepping alignment (SSA) matcher. Such alignment method can be integrated into both alignment-based framework and clustering-based framework. Extensive experiments validate the effectiveness of our proposed alignment method in these two frameworks. We achieve superior performance when the proposed alignment module is integrated into the clustering-based framework. Codes will be available at: http://github.com/Dingyuan-Zheng/HDS.