  The unsupervised domain adaptive person re-identification (re-ID) task has
been a challenge because, unlike the general domain adaptive tasks, there is no
overlap between the classes of source and target domain data in the person
re-ID, which leads to a significant domain gap. State-of-the-art unsupervised
re-ID methods train the neural networks using a memory-based contrastive loss.
However, performing contrastive learning by treating each unlabeled instance as
a class will lead to the problem of class collision, and the updating intensity
is inconsistent due to the difference in the number of instances of different
categories when updating in the memory bank. To address such problems, we
propose Prototype Dictionary Learning for person re-ID which is able to utilize
both source domain data and target domain data by one training stage while
avoiding the problem of class collision and the problem of updating intensity
inconsistency by cluster-level prototype dictionary learning. In order to
reduce the interference of domain gap on the model, we propose a local-enhance
module to improve the domain adaptation of the model without increasing the
number of model parameters. Our experiments on two large datasets demonstrate
the effectiveness of the prototype dictionary learning. 71.5\% mAP is achieved
in the Market-to-Duke task, which is a 2.3\% improvement compared to the
state-of-the-art unsupervised domain adaptive re-ID methods. It achieves 83.9\%
mAP in the Duke-to-Market task, which improves by 4.4\% compared to the
state-of-the-art unsupervised adaptive re-ID methods.
