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

Wenhao Zhang; Chang Liu; Chunjuan Bo; Dong Wang

doi:10.1117/12.2607183

28 October 2021 Attention mutual teaching network for unsupervised domain adaptation person re-identification

Wenhao Zhang, Chang Liu, Chunjuan Bo, Dong Wang

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841U (2021) https://doi.org/10.1117/12.2607183
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

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.

Citation Download Citation

Wenhao Zhang, Chang Liu, Chunjuan Bo, and Dong Wang "Attention mutual teaching network for unsupervised domain adaptation person re-identification", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841U (28 October 2021); https://doi.org/10.1117/12.2607183

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KEYWORDS

Content addressable memory

Data modeling

Performance modeling

Detection and tracking algorithms

Gallium nitride

Communication engineering

Convolution

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