  Unsupervised person re-identification (re-ID) has become an important topic
due to its potential to resolve the scalability problem of supervised re-ID
models. However, existing methods simply utilize pseudo labels from clustering
for supervision and thus have not yet fully explored the semantic information
in data itself, which limits representation capabilities of learned models. To
address this problem, we design a pretext task for unsupervised re-ID by
learning visual consistency from still images and temporal consistency during
training process, such that the clustering network can separate the images into
semantic clusters automatically. Specifically, the pretext task learns
semantically meaningful representations by maximizing the agreement between two
encoded views of the same image via a consistency loss in latent space.
Meanwhile, we optimize the model by grouping the two encoded views into same
cluster, thus enhancing the visual consistency between views. Experiments on
Market-1501, DukeMTMC-reID and MSMT17 datasets demonstrate that our proposed
approach outperforms the state-of-the-art methods by large margins.
