  In unsupervised person Re-ID, peer-teaching strategy leveraging two networks
to facilitate training has been proven to be an effective method to deal with
the pseudo label noise. However, training two networks with a set of noisy
pseudo labels reduces the complementarity of the two networks and results in
label noise accumulation. To handle this issue, this paper proposes a novel
Dual Clustering Co-teaching (DCCT) approach. DCCT mainly exploits the features
extracted by two networks to generate two sets of pseudo labels separately by
clustering with different parameters. Each network is trained with the pseudo
labels generated by its peer network, which can increase the complementarity of
the two networks to reduce the impact of noises. Furthermore, we propose dual
clustering with dynamic parameters (DCDP) to make the network adaptive and
robust to dynamically changing clustering parameters. Moreover, Consistent
Sample Mining (CSM) is proposed to find the samples with unchanged pseudo
labels during training for potential noisy sample removal. Extensive
experiments demonstrate the effectiveness of the proposed method, which
outperforms the state-of-the-art unsupervised person Re-ID methods by a
considerable margin and surpasses most methods utilizing camera information.
