Papers › Video Person Re-ID: Fantastic Techniques and Where to Find Them
Video Person Re-ID: Fantastic Techniques and Where to Find Them
Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish
The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Person Re-Identification | MARS | B-BOT + OSM + CL Centers* (Re-rank) | mAP | 88.5 | #1 of 21 | Archive leaderboard | report |
| Person Re-Identification | MARS | B-BOT + Attention and CL loss* | mAP | 82.9 | #10 of 21 | Archive leaderboard | report |
| Person Re-Identification | MARS | B-BOT + Attention and CL loss | Rank-1 | 88.6 | #21 of 21 | Archive leaderboard | report |
| Person Re-Identification | PRID2011 | B-BOT + Attention and CL loss* | Rank-1 | 96.6 | #1 of 13 | Archive leaderboard | report |
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