Papers › Learning Multiple Semantic Knowledge For Cross-Domain Unsupervised Vehicle Re-Identification
Learning Multiple Semantic Knowledge For Cross-Domain Unsupervised Vehicle Re-Identification
Huibing Wang, Jinjia Peng, Guangqi Jiang, Xianping Fu
Unsupervised Vehicle re-identification (reID) aims at searching the similar vehicles’ images from large unlabelled datasets captured in a multiple camera network, which is still a challenging task. In this paper, a multiple semantic knowledge learning approach is proposed to exploit the potential similarity of unlabeled samples, which builds multiple clusters from different views automatically with different cues. Specially, different from some existing works focus on the knowledge of one view, for each vehicle in the target domain, different semantic knowledge could be learned with the proposed focal drop network and several different labels can be assigned according these knowledge, which would be employed to train the vehicle reID model jointly. In addition, due to the unreliability of pseudo labels assigned by the clustering, the hard triplet center loss is proposed to take the difference of intra-cluster and inter-cluster into consideration for better training the unsupervised framework to adapt the unknown domain. Comprehensive experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset.
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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 | Veri-776 to VehicleID Large | ML | R-1 | 36.50 | #8 of 13 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Veri-776 to VehicleID Large | ML | R-10 | - | #8 of 13 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Veri-776 to VehicleID Large | ML | R-5 | 54.10 | #8 of 13 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Veri-776 to VehicleID Large | ML | mAP | 45.00 | #8 of 13 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Veri-776 to VehicleID Medium | ML | R-1 | 40.30 | #7 of 13 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Veri-776 to VehicleID Medium | ML | R-10 | - | #7 of 13 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Veri-776 to VehicleID Medium | ML | R-5 | 57.70 | #7 of 13 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Veri-776 to VehicleID Medium | ML | mAP | 48.70 | #7 of 13 | 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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