  Unsupervised person re-identification (re-ID) aims at closing the performance
gap to supervised methods. These methods build reliable relationship between
data points while learning representations. However, we empirically show that
the reason why they are successful is not only their label generation
mechanisms, but also their unexplored designs. By studying two unsupervised
person re-ID methods in a cross-method way, we point out a hard negative
problem is handled implicitly by their designs of data augmentations and PK
sampler respectively. In this paper, we find another simple solution for the
problem, i.e., taking more positives during training, by which we generate
pseudo-labels and update models in an iterative manner. Based on our findings,
we propose a contrastive learning method without a memory back for unsupervised
person re-ID. Our method works well on benchmark datasets and outperforms the
state-of-the-art methods. Code will be made available.
