Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification

• URL: http://arxiv.org/abs/2104.02265v1
• Date: Tue, 6 Apr 2021 03:12:11 GMT
• Title: Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification
• Authors: Suncheng Xiang, Yuzhuo Fu, Mengyuan Guan, Ting Liu
• Abstract summary: We propose a multiple co-teaching framework for domain adaptive person re-ID. Our method achieves competitive performance compared with the state-of-the-arts.
• Score: 12.106894735305714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation. A potential limitation of these clustering-based methods is that they always tend to introduce noisy labels, which will undoubtedly hamper the performance of our re-ID system. To handle this limitation, an intuitive solution is to utilize collaborative training to purify the pseudo label quality. However, there exists a challenge that the complementarity of two networks, which inevitably share a high similarity, becomes weakened gradually as training process goes on; worse still, these approaches typically ignore to consider the self-discrepancy of intra-class relations. To address this issue, in this letter, we propose a multiple co-teaching framework for domain adaptive person re-ID, opening up a promising direction about self-discrepancy problem under unsupervised condition. On top of that, a mean-teaching mechanism is leveraged to enlarge the difference and discover more complementary features. Comprehensive experiments conducted on several large-scale datasets show that our method achieves competitive performance compared with the state-of-the-arts.

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