CUPR: Contrastive Unsupervised Learning for Person Re-identification

Most of the current person re-identification (Re-ID) algorithms require a large labeled training dataset to obtain better results. For example, domain adaptation-based approaches rely heavily on limited real-world data to alleviate the problem of domain shift. However, such assumptions are impractical and rarely hold, since the data is not freely accessible and requires expensive annotation. To address this problem, we propose a novel pure unsupervised learning approach using contrastive learning (CUPR). Our framework is a simple iterative approach that learns strong high-level features from raw pixels using contrastive learning and then performs clustering to generate pseudo-labels. We demonstrate that CUPR outperforms the unsupervised and semi-supervised state-of-the-art methods on Market-1501 and DukeMTMC-reID datasets.