Papers › Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning...
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Yu-Jhe Li, Fu-En Yang, Yen-Cheng Liu, Yu-Ying Yeh, Xiaofei Du, Yu-Chiang Frank Wang
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training.
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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 | ARN | mAP | 39.4 | #20 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | ARN | rank-1 | 70.3 | #20 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | ARN | rank-10 | 86.3 | #20 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | ARN | rank-5 | 80.4 | #20 of 26 | 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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