Research map / Label-efficient and robust learning / Recognition and long-tail
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize contextual similarity alongside cosine similarity | Contextual metric learning · CUB | R@1: 71.9 vs 71.9 | No clear effect |
| Optimize contextual similarity alongside cosine similarity | Contextual metric learning · Cars | R@1: 91.5 vs 91.1 | Hurt |
| Optimize contextual similarity alongside cosine similarity | Contextual metric learning · SOP | R@1: 82.2 -> 82.6 | Helped |
| Add contextual loss to contrastive loss (ablation) | Contextual metric learning · CUB and Cars | R@1 | Helped |
| Add similarity regularization (ablation) | Contextual metric learning · Some datasets | R@1 | Helped |
| Correct distance gradients according to labels (ablation) | Contextual loss on CUB · CUB | Test R@1: 71.4 -> 65.4 | Hurt |
| Correct distance gradients according to labels (ablation) | Contextual loss on CUB · CUB | Train R@1: 87.0 -> 92.9 | Helped |
Closest papers by meaning
- Deep Metric Learning Beyond Binary Supervision (CVPR 2019 · similarity 0.89)
- Contextual Similarity Distillation for Asymmetric Image Retrieval (CVPR 2022 · similarity 0.88)
- Ranked List Loss for Deep Metric Learning (CVPR 2019 · similarity 0.88)
- Threshold-Consistent Margin Loss for Open-World Deep Metric Learning (ICLR 2024 · similarity 0.87)
- Contextual Document Embeddings (ICLR 2025 · similarity 0.85)
- Exploiting Distribution Constraints for Scalable and Efficient Image Retrieval (ICLR 2025 · similarity 0.85)
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