Papers › Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification
Jingya Wang, Xiatian Zhu, Shaogang Gong, Wei Li
Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair. This significantly limits their scalability and usability in real-world large scale deployments with the need for performing re-id across many camera views. To address this scalability problem, we develop a novel deep learning method for transferring the labelled information of an existing dataset to a new unseen (unlabelled) target domain for person re-id without any supervised learning in the target domain. Specifically, we introduce an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identitydiscriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain). Extensive comparative evaluations validate the superiority of this new TJ-AIDL model for unsupervised person re-id over a wide range of state-of-the-art methods on four challenging benchmarks including VIPeR, PRID, Market-1501, and DukeMTMC-ReID.
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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 | TJ-AIDL | mAP | 26.5 | #24 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | TJ-AIDL | rank-1 | 58.2 | #24 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | TJ-AIDL | rank-10 | 81.1 | #24 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | TJ-AIDL | rank-5 | 74.8 | #24 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | TJ-AIDL | mAP | 23.0 | #23 of 25 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | TJ-AIDL | rank-1 | 44.3 | #23 of 25 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | TJ-AIDL | rank-10 | 65.0 | #23 of 25 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | TJ-AIDL | rank-5 | 59.6 | #23 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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