HyperAI超神经

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HyperAI超神经

Image Retrieval On Oxf105K

评估指标

MAP

评测结果

各个模型在此基准测试上的表现结果

		Paper Title	Repository
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超神经