Plausible Proxy Mining with Credibility for Unsupervised Person Re-identification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Dingyuan Zheng
  • Jimin Xiao
  • Mingjie Sun
  • Huihui Bai
  • Junhui Hou

Related Research Unit(s)


Original languageEnglish
Number of pages12
Journal / PublicationIEEE Transactions on Circuits and Systems for Video Technology
Publication statusOnline published - 27 Dec 2022


One effective way to address unsupervised person re-identification is to use a clustering-based contrastive learning approach. Existing state-of-the-art methods adopt clustering algorithms (e.g., DBSCAN [1]) and camera ID information to divide all person images into several camera-aware proxies. Then, for each person image, the extracted feature representation is pulled closer to the centroids of its pseudo-positive proxies (the proxies that share the same pseudo-identity label with this image) and pushed away from the centroids of other pseudo-negative proxies (the proxies that share the different pseudo-identity label with this image). However, the quality of the proxy centroid is significantly affected by the proxy impurity issue and thus deteriorates the learned feature representations. On the premise that we cannot introduce superior supervision signals by thoroughly solving the proxy impurity issue, for a person image, identifying its plausible proxies: the pseudo-negative proxies which potentially include its wrongly-clustered instances (the instances with the same ground-truth identity with this image), and further fixing the resulted incorrect supervision signals become an urgent and challenging problem. This paper proposes a simple yet effective approach to address this problem. With a given image, our method can effectively locate its plausible proxies. Then we introduce credibility to measure how much we should treat the centroid of each mined plausible proxy as a positive supervision signal rather than entirely negative. Extensive experiments on three widely-used person re-ID datasets validate the effectiveness of our proposed approach. Codes will be available at:

Research Area(s)

  • Person Re-identification, Clustering Algorithm, Proxy Impurity Issue, Contrastive Learning, Plausible Proxy, Supervision Signals