HHCL-ReID
MAGIST-Algorithm
| HHCL-ReID | MAGIST-Algorithm | |
|---|---|---|
| 1 | 1 | |
| 133 | 5 | |
| - | - | |
| 0.0 | 3.8 | |
| almost 2 years ago | 9 months ago | |
| Python | Python | |
| - | GNU General Public License v3.0 only |
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HHCL-ReID
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Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled data is of central importance to Re-ID. Recently, more attention has been paid to unsupervised Re-ID algorithms based on clustered pseudo-label. However, the previous approaches did not fully exploit information of hard samples, simply using cluster centroid or all instances for contrastive learning. In this paper, we propose a Hard-sample Guided Hybrid Contrast Learning (HHCL) approach combining cluster-level loss with instance-level loss for unsupervised person Re-ID. Our approach applies cluster centroid contrastive loss to ensure that the network is updated in a more stable way. Meanwhile, introduction of a hard instance contrastive loss further mines the discriminative information. Extensive experiments on two popular large-scale Re-ID benchmarks demonstrate that our HHCL outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID. The code of our work is available soon at https://github.com/bupt-ai-cz/HHCL-ReID.
MAGIST-Algorithm
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