Reproduce “Cluster Contrast for Unsupervised Person Re-Identification” on OpenResearch
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About this paper
Zuozhuo Dai, Guangyuan Wang, Weihao Yuan, Xiaoli Liu, Siyu Zhu, Ping Tan
Cluster Contrast introduces a purely unsupervised person re-identification method that uses a cluster-level memory dictionary and momentum updates for cluster representations. This approach addresses feature inconsistency in prior methods and achieves state-of-the-art performance, with Market-1501 reaching 83.0% mAP and 92.9% Rank-1 accuracy.
Key insights
- Aggregating features at the cluster level and applying momentum updates to these cluster representations effectively mitigates feature inconsistency in memory-based unsupervised Re-ID.
- Learning features by contrasting against cluster centroids rather than individual instances leads to more stable and robust training signals.
- The proposed method yields more discriminative feature embeddings with smaller intra-class and larger inter-class distances, indicating improved representation quality.
Summary by alphaXiv.
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