  Unsupervised Re-ID methods aim at learning robust and discriminative features
from unlabeled data. However, existing methods often ignore the relationship
between module parameters of Re-ID framework and feature distributions, which
may lead to feature misalignment and hinder the model performance. To address
this problem, we propose a dynamic clustering and cluster contrastive learning
(DCCC) method. Specifically, we first design a dynamic clustering parameters
scheduler (DCPS) which adjust the hyper-parameter of clustering to fit the
variation of intra- and inter-class distances. Then, a dynamic cluster
contrastive learning (DyCL) method is designed to match the cluster
representation vectors' weights with the local feature association. Finally, a
label smoothing soft contrastive loss ($L_{ss}$) is built to keep the balance
between cluster contrastive learning and self-supervised learning with low
computational consumption and high computational efficiency. Experiments on
several widely used public datasets validate the effectiveness of our proposed
DCCC which outperforms previous state-of-the-art methods by achieving the best
performance.
