Papers › Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
Ruijie Quan, Xuanyi Dong, Yu Wu, Linchao Zhu, Yi Yang
Prevailing deep convolutional neural networks (CNNs) for person re-IDentification (reID) are usually built upon ResNet or VGG backbones, which were originally designed for classification. Because reID is different from classification, the architecture should be modified accordingly. We propose to automatically search for a CNN architecture that is specifically suitable for the reID task. There are three aspects to be tackled. First, body structural information plays an important role in reID but it is not encoded in backbones. Second, Neural Architecture Search (NAS) automates the process of architecture design without human effort, but no existing NAS methods incorporate the structure information of input images. Third, reID is essentially a retrieval task but current NAS algorithms are merely designed for classification. To solve these problems, we propose a retrieval-based search algorithm over a specifically designed reID search space, named Auto-ReID. Our Auto-ReID enables the automated approach to find an efficient and effective CNN architecture for reID. Extensive experiments demonstrate that the searched architecture achieves state-of-the-art performance while reducing 50% parameters and 53% FLOPs compared to others.
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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 | CUHK03 detected | Auto-ReID (ICCV'19) | MAP | 69.3 | #9 of 19 | Archive leaderboard | report |
| Person Re-Identification | CUHK03 detected | Auto-ReID (ICCV'19) | Rank-1 | 73.3 | #9 of 19 | Archive leaderboard | report |
| Person Re-Identification | CUHK03 labeled | Auto-ReID (ICCV'19) | MAP | 73.0 | #12 of 21 | Archive leaderboard | report |
| Person Re-Identification | CUHK03 labeled | Auto-ReID (ICCV'19) | Rank-1 | 77.9 | #12 of 21 | Archive leaderboard | report |
| Person Re-Identification | DukeMTMC-reID | Auto-ReID(RK) | Rank-1 | 91.4 | #13 of 94 | Archive leaderboard | report |
| Person Re-Identification | DukeMTMC-reID | Auto-ReID(RK) | mAP | 89.2 | #13 of 94 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | Auto-ReID(RK) | Rank-1 | 95.4 | #58 of 135 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | Auto-ReID(RK) | mAP | 94.2 | #58 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.
Methods
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