Image Retrieval On Oxf105K
Métriques
MAP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | MAP | Paper Title | Repository |
|---|---|---|---|
| R-MAC | 61.6% | Particular object retrieval with integral max-pooling of CNN activations | |
| DIR+QE* | 87.8% | Deep Image Retrieval: Learning global representations for image search | |
| DELF+FT+ATT+DIR+QE | 88.5% | Large-Scale Image Retrieval with Attentive Deep Local Features | |
| R-MAC+R+QE | 73.2% | Particular object retrieval with integral max-pooling of CNN activations | |
| siaMAC+QE* | 77.9% | CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples | |
| SIFT+IME layer | 31.3% | Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval | |
| CNN+IME layer | 87.2% | Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval | |
| DELF+FT+ATT | 82.6% | Large-Scale Image Retrieval with Attentive Deep Local Features | |
| Offline Diffusion | 95.2% | Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing |
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