  Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at
adapting a model trained on a labeled source domain to an unlabeled target
domain. State-of-the-art object Re-ID approaches adopt clustering algorithms to
generate pseudo-labels for the unlabeled target domain. However, the inevitable
label noise caused by the clustering procedure significantly degrades the
discriminative power of Re-ID model. To address this problem, we propose an
uncertainty-aware clustering framework (UCF) for UDA tasks. First, a novel
hierarchical clustering scheme is proposed to promote clustering quality.
Second, an uncertainty-aware collaborative instance selection method is
introduced to select images with reliable labels for model training. Combining
both techniques effectively reduces the impact of noisy labels. In addition, we
introduce a strong baseline that features a compact contrastive loss. Our UCF
method consistently achieves state-of-the-art performance in multiple UDA tasks
for object Re-ID, and significantly reduces the gap between unsupervised and
supervised Re-ID performance. In particular, the performance of our
unsupervised UCF method in the MSMT17$\to$Market1501 task is better than that
of the fully supervised setting on Market1501. The code of UCF is available at
https://github.com/Wang-pengfei/UCF.
