Papers › Unsupervised multi-source domain adaptation for person re-identification via feature...
Unsupervised multi-source domain adaptation for person re-identification via feature fusion and pseudo-label refinement
Qing Tian, Yao Cheng, Sizhen He, Jixin Sun
The objective of unsupervised domain adaptation (UDA) for person re-identification (re-ID) is to associate person in images captured from heterogeneous camera perspectives. Currently, mainstream UDA methods for person re-ID are mainly conducted in single-source and single-target domain scenarios. Moreover, most of these methods do not take the repercussions of pseudo-label noise on model performance into consideration. Therefore, we put forward an unsupervised multi-source domain adaptation (UMDA) method for person re-ID via feature fusion and pseudo-label refinement. Firstly, our method is designed for scenarios where there exist several source domains and only one target domain. We suggest using feature fusion techniques to minimize the domain disparity among the source domains, and employ pseudo-label refinement techniques to ameliorate the ramifications of label noise on model predictions. To substantiate the effectiveness of the recommended methodology, we carry out a succession of experiments on multiple datasets. The advantage and preeminence of our proposed method can be manifested by the experimental outcomes.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Domain Adaptation | Market to MSMT | UMDA | mAP | 32.7 | #5 of 17 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to MSMT | UMDA | rank-1 | 62.4 | #5 of 17 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to MSMT | UMDA | rank-10 | 78.4 | #5 of 17 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to MSMT | UMDA | rank-5 | 72.7 | #5 of 17 | 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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