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Image Retrieval On Cub 200 2011
Metrics
R@1
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| CGD (MG/SG) | 79.2 | Combination of Multiple Global Descriptors for Image Retrieval | |
| ROADMAP (Deit-B) | 77.4 | Robust and Decomposable Average Precision for Image Retrieval | |
| ProxyNCA++ | 72.2 | ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis | |
| ROADMAP (ResNet-50) | 68.5 | Robust and Decomposable Average Precision for Image Retrieval | |
| MES-Loss | 67.03 | MES-Loss: Mutually equidistant separation metric learning loss function | - |
| MS512 | 65.7 | Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning | |
| NormSoftmax2048 (ResNet-50) | 65.3 | Classification is a Strong Baseline for Deep Metric Learning | |
| EPSHN512 | 64.9 | Improved Embeddings with Easy Positive Triplet Mining |
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