  Clustering-based unsupervised domain adaptive (UDA) person re-identification
(ReID) reduces exhaustive annotations. However, owing to unsatisfactory feature
embedding and imperfect clustering, pseudo labels for target domain data
inherently contain an unknown proportion of wrong ones, which would mislead
feature learning. In this paper, we propose an approach named probabilistic
uncertainty guided progressive label refinery (P$^2$LR) for domain adaptive
person re-identification. First, we propose to model the labeling uncertainty
with the probabilistic distance along with ideal single-peak distributions. A
quantitative criterion is established to measure the uncertainty of pseudo
labels and facilitate the network training. Second, we explore a progressive
strategy for refining pseudo labels. With the uncertainty-guided alternative
optimization, we balance between the exploration of target domain data and the
negative effects of noisy labeling. On top of a strong baseline, we obtain
significant improvements and achieve the state-of-the-art performance on four
UDA ReID benchmarks. Specifically, our method outperforms the baseline by 6.5%
mAP on the Duke2Market task, while surpassing the state-of-the-art method by
2.5% mAP on the Market2MSMT task.
