Papers › Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
Jianyuan Guo, Yuhui Yuan, Lang Huang, Chao Zhang, Jinge Yao, Kai Han
Person re-identification is a challenging task due to various complex factors. Recent studies have attempted to integrate human parsing results or externally defined attributes to help capture human parts or important object regions. On the other hand, there still exist many useful contextual cues that do not fall into the scope of predefined human parts or attributes. In this paper, we address the missed contextual cues by exploiting both the accurate human parts and the coarse non-human parts. In our implementation, we apply a human parsing model to extract the binary human part masks \emph{and} a self-attention mechanism to capture the soft latent (non-human) part masks. We verify the effectiveness of our approach with new state-of-the-art performances on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03. Our implementation is available at https://github.com/ggjy/P2Net.pytorch.
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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 | DukeMTMC-reID | P2-Net (triplet loss) | Rank-1 | 86.5 | #63 of 94 | Archive leaderboard | report |
| Person Re-Identification | DukeMTMC-reID | P2-Net (triplet loss) | Rank-10 | 95 | #63 of 94 | Archive leaderboard | report |
| Person Re-Identification | DukeMTMC-reID | P2-Net (triplet loss) | Rank-5 | 93.1 | #63 of 94 | Archive leaderboard | report |
| Person Re-Identification | DukeMTMC-reID | P2-Net (triplet loss) | mAP | 73.1 | #63 of 94 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | P2-Net (triplet loss) | Rank-1 | 95.2 | #69 of 135 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | P2-Net (triplet loss) | Rank-5 | 98.2 | #69 of 135 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | P2-Net (triplet loss) | mAP | 85.6 | #69 of 135 | 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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