Soft pseudo-Label shrinkage for unsupervised domain adaptive person re-identification

Abstract

One effective way to tackle unsupervised domain adaptation (UDA) on person re-identification (Re-ID) is to use clustering-based self-training approach, where a model is trained with hard pseudo-labels obtained from a clustering method. Using a hard pseudo-label, a sample is assigned to the cluster with the highest probability, which is sensitive to the incorrect clustering result due to imperfect clustering algorithms. Soft pseudo-labels can mitigate this issue by representing the sample with the full range of class probabilities from all clusters. Specifically, soft pseudo-labels comprise probabilities of full range classes, because they consider both the hard samples and easy samples. This will distract the model from learning more discriminative features in the hard examples. To solve this issue, we propose a coarse-to-fine refinement mechanism to produce robust refined soft pseudo-labels by progressively focusing more on the hard samples while less on the easy samples. The proposed refined soft pseudo-labels can be readily integrated into cross-entropy loss as a strong supervision to guide the model to learn more discriminative features. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art unsupervised domain adaptation approaches on person Re-ID with a considerable margin. Code will be available at: http://github.com/Dingyuan-Zheng/ctf-UDA.