Papers › Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Yu-Jhe Li, Ci-Siang Lin, Yan-Bo Lin, Yu-Chiang Frank Wang
Person re-identification (re-ID) aims at recognizing the same person from images taken across different cameras. To address this challenging task, existing re-ID models typically rely on a large amount of labeled training data, which is not practical for real-world applications. To alleviate this limitation, researchers now targets at cross-dataset re-ID which focuses on generalizing the discriminative ability to the unlabeled target domain when given a labeled source domain dataset. To achieve this goal, our proposed Pose Disentanglement and Adaptation Network (PDA-Net) aims at learning deep image representation with pose and domain information properly disentangled. With the learned cross-domain pose invariant feature space, our proposed PDA-Net is able to perform pose disentanglement across domains without supervision in identities, and the resulting features can be applied to cross-dataset re-ID. Both of our qualitative and quantitative results on two benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art cross-dataset Re-ID approaches.
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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 | Duke to Market | PDA-Net | mAP | 47.6 | #18 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | PDA-Net | rank-1 | 75.2 | #18 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | PDA-Net | rank-10 | 90.2 | #18 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | PDA-Net | rank-5 | 86.3 | #18 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | PDA-Net | mAP | 45.1 | #17 of 25 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | PDA-Net | rank-1 | 63.2 | #17 of 25 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | PDA-Net | rank-10 | 82.5 | #17 of 25 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | PDA-Net | rank-5 | 77.0 | #17 of 25 | 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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