Metric Learning On Stanford Online Products 1
評価指標
R@1
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | R@1 |
|---|---|
| stir-siamese-transformer-for-image-retrieval | 86.5 |
| improved-embeddings-with-easy-positive | 78.3 |
| hyperbolic-vision-transformers-combining | 85.1 |
| non-isotropy-regularization-for-proxy-based | 80.7 |
| metric-learning-cross-entropy-vs-pairwise | 81.1 |
| hierarchical-average-precision-training-for | 81.8 |
| cross-modal-retrieval-with-querybank | 78.1 |
| the-group-loss-for-deep-metric-learning | 75.7 |
| robust-and-decomposable-average-precision-for | 83.1 |
| towards-interpretable-deep-metric-learning | 79.26 |
| s2sd-simultaneous-similarity-based-self | 81.0 |
| recall-k-surrogate-loss-with-large-batches | 88.0 |
| unicom-universal-and-compact-representation | 91.2 |
| recall-k-surrogate-loss-with-large-batches | 82.7 |
| proxy-anchor-loss-for-deep-metric-learning | 80.3 |
| pads-policy-adapted-sampling-for-visual | 76.5 |
| recall-k-surrogate-loss-with-large-batches | 85.1 |
| stir-siamese-transformer-for-image-retrieval | 88.3 |
| mic-mining-interclass-characteristics-for | 77.2 |
| integrating-language-guidance-into-vision | 81.3 |
| das-densely-anchored-sampling-for-deep-metric | 80.59 |
| attributable-visual-similarity-learning | 79.6 |
| hierarchical-average-precision-training-for | 81.0 |
| hyperbolic-vision-transformers-combining | 85.9 |
| robust-and-decomposable-average-precision-for | 86.0 |
| circle-loss-a-unified-perspective-of-pair | 78.3 |
| calibrated-neighborhood-aware-confidence | 81.2 |
| hard-negative-examples-are-hard-but-useful | 81.6 |
| center-contrastive-loss-for-metric-learning | 83.10 |
| diva-diverse-visual-feature-aggregation | 79.6 |
| proxynca-revisiting-and-revitalizing-proxy | 80.7 |
| it-takes-two-to-tango-mixup-for-deep-metric | 81.3 |
| dissecting-the-impact-of-different-loss | 82.3 |