Papers › Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification
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
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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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