Attention mutual teaching network for unsupervised domain adaptation person re-identification

Abstract

Person re-identification (ReID) is an important task in computer vision. Most methods based on supervised strategies have achieved high performance. However, performance cannot be maintained when these methods are applied without labels because styles in different scenes exhibit considerable discrepancy. To address this problem, we propose an attention mutual teaching (AMT) network for unsupervised domain adaptation person ReID. The AMT method improves the performance of a model through iterative clustering and retraining. Meanwhile, two attention modules can teach each other to reduce clustering noise. We conduct extensive experiments on the Market-1501 and DukeMTMC-reID datasets. The experiments show that our approach performs better than state-of-the-art unsupervised methods.