  For most unsupervised person re-identification (re-ID), people often adopt
unsupervised domain adaptation (UDA) method. UDA often train on the labeled
source dataset and evaluate on the target dataset, which often focuses on
learning differences between the source dataset and the target dataset to
improve the generalization of the model. Base on these, we explore how to make
use of the similarity of samples to conduct a fully unsupervised method which
just trains on the unlabeled target dataset. Concretely, we propose a
hierarchical clustering-guided re-ID (HCR) method. We use hierarchical
clustering to generate pseudo labels and use these pseudo labels as monitors to
conduct the training. In order to exclude hard examples and promote the
convergence of the model, We use PK sampling in each iteration, which randomly
selects a fixed number of samples from each cluster for training. We evaluate
our model on Market-1501, DukeMTMC-reID and MSMT17. Results show that HCR gets
the state-of-the-arts and achieves 55.3% mAP on Market-1501 and 46.8% mAP on
DukeMTMC-reID. Our code will be released soon.
