Papers › Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
Yang Zou, Xiaodong Yang, Zhiding Yu, B. V. K. Vijaya Kumar, Jan Kautz
Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. Recently, there has been a growing interest in using unsupervised domain adaptation to address this scalability issue. Existing methods typically conduct adaptation on the representation space that contains both id-related and id-unrelated factors, thus inevitably undermining the adaptation efficacy of id-related features. In this paper, we seek to improve adaptation by purifying the representation space to be adapted. To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively. Our model involves a disentangling module that encodes cross-domain images into a shared appearance space and two separate structure spaces, and an adaptation module that performs adversarial alignment and self-training on the shared appearance space. The two modules are co-designed to be mutually beneficial. Extensive experiments demonstrate that the proposed joint learning framework outperforms the state-of-the-art methods by clear margins.
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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 | DG-Net++ | mAP | 22.1 | #10 of 17 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to MSMT | DG-Net++ | rank-1 | 48.4 | #10 of 17 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to MSMT | DG-Net++ | rank-10 | 66.1 | #10 of 17 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to MSMT | DG-Net++ | rank-5 | 60.9 | #10 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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