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Image Retrieval On Oxf105K

Metrics

MAP

Results

Performance results of various models on this benchmark

Paper Title
Offline Diffusion95.2%Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
DELF+FT+ATT+DIR+QE88.5%Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*87.8%Deep Image Retrieval: Learning global representations for image search
CNN+IME layer87.2%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
DELF+FT+ATT82.6%Large-Scale Image Retrieval with Attentive Deep Local Features
siaMAC+QE*77.9%CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
R-MAC+R+QE73.2%Particular object retrieval with integral max-pooling of CNN activations
R-MAC61.6%Particular object retrieval with integral max-pooling of CNN activations
SIFT+IME layer31.3%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
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Image Retrieval On Oxf105K | SOTA | HyperAI