Papers › Center Contrastive Loss for Metric Learning
Center Contrastive Loss for Metric Learning
Bolun Cai, Pengfei Xiong, Shangxuan Tian
Contrastive learning is a major studied topic in metric learning. However, sampling effective contrastive pairs remains a challenge due to factors such as limited batch size, imbalanced data distribution, and the risk of overfitting. In this paper, we propose a novel metric learning function called Center Contrastive Loss, which maintains a class-wise center bank and compares the category centers with the query data points using a contrastive loss. The center bank is updated in real-time to boost model convergence without the need for well-designed sample mining. The category centers are well-optimized classification proxies to re-balance the supervisory signal of each class. Furthermore, the proposed loss combines the advantages of both contrastive and classification methods by reducing intra-class variations and enhancing inter-class differences to improve the discriminative power of embeddings. Our experimental results, as shown in Figure 1, demonstrate that a standard network (ResNet50) trained with our loss achieves state-of-the-art performance and faster convergence.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Metric Learning | CARS196 | CCL (ResNet-50) | R@1 | 91.02 | #7 of 36 | Archive leaderboard | report |
| Metric Learning | CUB-200-2011 | CCL (ResNet-50) | R@1 | 73.45 | #5 of 30 | Archive leaderboard | report |
| Metric Learning | In-Shop | CCL (ResNet-50) | R@1 | 92.31 | #6 of 15 | Archive leaderboard | report |
| Metric Learning | Stanford Online Products | CCL (ResNet-50) | R@1 | 83.10 | #10 of 33 | 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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