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Image Retrieval On Oxf5K

Metrics

MAP

Results

Performance results of various models on this benchmark

Model Name
MAP
Paper TitleRepository
IsoMap [32]77.9%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval-
Offline Diffusion96.2%Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing-
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-
PCA [51]82.6%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-
DELF+FT+ATT+DIR+QE90.0%Large-Scale Image Retrieval with Attentive Deep Local Features-
IME83.5%Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval-
CNN+IME layer92%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-
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Image Retrieval On Oxf5K | SOTA | HyperAI