Papers › Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification

Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification

14 Jun 2021arXiv:2106.07204archive 2025-07-28

Chih-Ting Liu, Man-Yu Lee, Tsai-Shien Chen, Shao-Yi Chien

Person re-identification (re-ID) has received great success with the supervised learning methods. However, the task of unsupervised cross-domain re-ID is still challenging. In this paper, we propose a Hard Samples Rectification (HSR) learning scheme which resolves the weakness of original clustering-based methods being vulnerable to the hard positive and negative samples in the target unlabelled dataset. Our HSR contains two parts, an inter-camera mining method that helps recognize a person under different views (hard positive) and a part-based homogeneity technique that makes the model discriminate different persons but with similar appearance (hard negative). By rectifying those two hard cases, the re-ID model can learn effectively and achieve promising results on two large-scale benchmarks.

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Tasks

ClusteringPerson Re-IdentificationUnsupervised Domain AdaptationUnsupervised Person Re-Identification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Person Re-Identification DukeMTMC-reID->Market-1501 HSR (Ours) Rank-1 85.3 #3 of 8 Archive leaderboard report
Unsupervised Person Re-Identification DukeMTMC-reID->Market-1501 HSR (Ours) mAP 65.2 #3 of 8 Archive leaderboard report
Unsupervised Person Re-Identification Market-1501->DukeMTMC-reID HSR (Ours) Rank-1 76.1 #4 of 7 Archive leaderboard report
Unsupervised Person Re-Identification Market-1501->DukeMTMC-reID HSR (Ours) mAP 58.1 #4 of 7 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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