  Recently, many approaches tackle the Unsupervised Domain Adaptive person
re-identification (UDA re-ID) problem through pseudo-label-based contrastive
learning. During training, a uni-centroid representation is obtained by simply
averaging all the instance features from a cluster with the same pseudo label.
However, a cluster may contain images with different identities (label noises)
due to the imperfect clustering results, which makes the uni-centroid
representation inappropriate. In this paper, we present a novel Multi-Centroid
Memory (MCM) to adaptively capture different identity information within the
cluster. MCM can effectively alleviate the issue of label noises by selecting
proper positive/negative centroids for the query image. Moreover, we further
propose two strategies to improve the contrastive learning process. First, we
present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully
explore intradomain information by comparing samples only from the same domain.
Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant
and informative negative samples. We integrate MCM, DSCL, and SONI into a
unified framework named Multi-Centroid Representation Network (MCRN). Extensive
experiments demonstrate the superiority of MCRN over state-of-the-art
approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks.
