  Unsupervised object re-identification targets at learning discriminative
representations for object retrieval without any annotations. Clustering-based
methods conduct training with the generated pseudo labels and currently
dominate this research direction. However, they still suffer from the issue of
pseudo label noise. To tackle the challenge, we propose to properly estimate
pseudo label similarities between consecutive training generations with
clustering consensus and refine pseudo labels with temporally propagated and
ensembled pseudo labels. To the best of our knowledge, this is the first
attempt to leverage the spirit of temporal ensembling to improve classification
with dynamically changing classes over generations. The proposed pseudo label
refinery strategy is simple yet effective and can be seamlessly integrated into
existing clustering-based unsupervised re-identification methods. With our
proposed approach, state-of-the-art method can be further boosted with up to
8.8% mAP improvements on the challenging MSMT17 dataset.
