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Image Retrieval On Oxf105K
Image Retrieval On Oxf105K
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MAP
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
MAP
Paper Title
Offline Diffusion
95.2%
Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
DELF+FT+ATT+DIR+QE
88.5%
Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*
87.8%
Deep Image Retrieval: Learning global representations for image search
CNN+IME 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
siaMAC+QE*
77.9%
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
R-MAC+R+QE
73.2%
Particular object retrieval with integral max-pooling of CNN activations
R-MAC
61.6%
Particular object retrieval with integral max-pooling of CNN activations
SIFT+IME layer
31.3%
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
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Image Retrieval On Oxf105K | SOTA | HyperAI