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Image Retrieval On Oxf5K

Metrics

MAP

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Offline Diffusion	96.2%	Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
CNN+IME layer	92%	Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
DELF+FT+ATT+DIR+QE	90.0%	Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*	89%	Deep Image Retrieval: Learning global representations for image search
DELF+FT+ATT	83.8%	Large-Scale Image Retrieval with Attentive Deep Local Features
IME	83.5%	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 layer	62.2%	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