  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 paper, 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.
