Image Retrieval On Roxford Hard
Métriques
mAP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | mAP |
|---|---|
| large-scale-image-retrieval-with-attentive | 43.1 |
| particular-object-retrieval-with-integral-max | 18.0 |
| instance-level-image-retrieval-using | 64 |
| hypergraph-propagation-and-community | 73 |
| learning-token-based-representation-for-image | 66.57 |
| revisiting-oxford-and-paris-large-scale-image | 13.2 |
| revisiting-oxford-and-paris-large-scale-image | 35.8 |
| large-scale-image-retrieval-with-attentive | 50.3 |
| fine-tuning-cnn-image-retrieval-with-no-human | 38.5 |
| revisiting-oxford-and-paris-large-scale-image | 36.4 |
| emerging-properties-in-self-supervised-vision | 24.3 |
| learning-super-features-for-image-retrieval-1 | 61.2 |
| revisiting-oxford-and-paris-large-scale-image | 49.7 |
| revisiting-oxford-and-paris-large-scale-image | 36.7 |
| google-landmarks-dataset-v2-a-large-scale | 51.6 |
| particular-object-retrieval-with-integral-max | 32.4 |
| revisiting-oxford-and-paris-large-scale-image | 35.4 |
| learning-and-aggregating-deep-local | 56.9 |
| cross-dimensional-weighting-for-aggregated | 13.3 |
| revisiting-oxford-and-paris-large-scale-image | 41.3 |
| global-features-are-all-you-need-for-image | 80.2 |
| 2408-03282 | 80 |
| aggregating-deep-convolutional-features-for | 12.4 |