AAMT: Adversarial Attack-Driven Mutual Teaching for Source-Free Domain-Adaptive Person Reidentification

Xiaofeng Qu, Huaxiang Zhang, Lei Zhu, Liqiang Nie, Li Liu

Abstract: Conventional domain adaptive (DA) methods for person re-identification (ReID) face knowledge transfer challenges when labeled data from the source domain cannot be accessed due to privacy constraints. Although the methods operating under source-absent DA settings attempt to address this challenge by using models pretrained on the source domain in their mutual teaching frameworks, failing to capture domain divergence in scenarios in which the source data are completely inaccessible can simultaneously introduce issues related to mutual convergence. In response, we introduce an adversarial attacks-driven mutual teaching (AAMT) framework as an innovative and applicable source-free DA person ReID scheme. Specifically, we first carefully develop a perturbation generator to generate source-style adversarial examples by leveraging a pretrained source model. Then, these diverse adversarial examples are employed to attack the mutual teaching model, implicitly measuring the domain divergence. Accordingly, we design a contrastive learning loss to enlarge the differences between the training pairs and further mitigate the mutual convergence issue. Extensive experiments demonstrate that AAMT outperforms the existing methods under both conventional and source-absent DA settings, achieving state-of-the-art performance.