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Image Retrieval On Oxf5K

Metrics

MAP

Results

Performance results of various models on this benchmark

Paper Title
Offline Diffusion96.2%Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
CNN+IME layer92%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
DELF+FT+ATT+DIR+QE90.0%Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*89%Deep Image Retrieval: Learning global representations for image search
DELF+FT+ATT83.8%Large-Scale Image Retrieval with Attentive Deep Local Features
IME83.5%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
siaMAC+QE*82.9%CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
PCA [51]82.6%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
IsoMap [32]77.9%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
SIFT+IME layer62.2%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
LLE [33]51.7%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
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Image Retrieval On Oxf5K | SOTA | HyperAI