  Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at
adapting the model trained on a labeled source-domain dataset to a
target-domain dataset without any further annotations. Most successful UDA-ReID
approaches combine clustering-based pseudo-label prediction with representation
learning and perform the two steps in an alternating fashion. However, offline
interaction between these two steps may allow noisy pseudo labels to
substantially hinder the capability of the model. In this paper, we propose a
Group-aware Label Transfer (GLT) algorithm, which enables the online
interaction and mutual promotion of pseudo-label prediction and representation
learning. Specifically, a label transfer algorithm simultaneously uses pseudo
labels to train the data while refining the pseudo labels as an online
clustering algorithm. It treats the online label refinery problem as an optimal
transport problem, which explores the minimum cost for assigning M samples to N
pseudo labels. More importantly, we introduce a group-aware strategy to assign
implicit attribute group IDs to samples. The combination of the online label
refining algorithm and the group-aware strategy can better correct the noisy
pseudo label in an online fashion and narrow down the search space of the
target identity. The effectiveness of the proposed GLT is demonstrated by the
experimental results (Rank-1 accuracy) for Market1501$\to$DukeMTMC (82.0\%) and
DukeMTMC$\to$Market1501 (92.2\%), remarkably closing the gap between
unsupervised and supervised performance on person re-identification.
