  Unsupervised person re-identification (Re-ID) is a promising and very
challenging research problem in computer vision. Learning robust and
discriminative features with unlabeled data is of central importance to Re-ID.
Recently, more attention has been paid to unsupervised Re-ID algorithms based
on clustered pseudo-label. However, the previous approaches did not fully
exploit information of hard samples, simply using cluster centroid or all
instances for contrastive learning. In this paper, we propose a Hard-sample
Guided Hybrid Contrast Learning (HHCL) approach combining cluster-level loss
with instance-level loss for unsupervised person Re-ID. Our approach applies
cluster centroid contrastive loss to ensure that the network is updated in a
more stable way. Meanwhile, introduction of a hard instance contrastive loss
further mines the discriminative information. Extensive experiments on two
popular large-scale Re-ID benchmarks demonstrate that our HHCL outperforms
previous state-of-the-art methods and significantly improves the performance of
unsupervised person Re-ID. The code of our work is available soon at
https://github.com/bupt-ai-cz/HHCL-ReID.
