  Unsupervised person re-identification is a challenging and promising task in
computer vision. Nowadays unsupervised person re-identification methods have
achieved great progress by training with pseudo labels. However, how to purify
feature and label noise is less explicitly studied in the unsupervised manner.
To purify the feature, we take into account two types of additional features
from different local views to enrich the feature representation. The proposed
multi-view features are carefully integrated into our cluster contrast learning
to leverage more discriminative cues that the global feature easily ignored and
biased. To purify the label noise, we propose to take advantage of the
knowledge of teacher model in an offline scheme. Specifically, we first train a
teacher model from noisy pseudo labels, and then use the teacher model to guide
the learning of our student model. In our setting, the student model could
converge fast with the supervision of the teacher model thus reduce the
interference of noisy labels as the teacher model greatly suffered. After
carefully handling the noise and bias in the feature learning, our purification
modules are proven to be very effective for unsupervised person
re-identification. Extensive experiments on three popular person
re-identification datasets demonstrate the superiority of our method.
Especially, our approach achieves a state-of-the-art accuracy 85.8\% @mAP and
94.5\% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under
the fully unsupervised setting. The code will be released.
