Image Retrieval On Par106K

Metrics

mAP

Results

Performance results of various models on this benchmark

		Paper Title
Offline Diffusion	96.2%	Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
DELF+FT+ATT+DIR+QE	92.8%	Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*	90.5%	Deep Image Retrieval: Learning global representations for image search
DELF+FT+ATT	81.7%	Large-Scale Image Retrieval with Attentive Deep Local Features
R-MAC+R+QE	79.8%	Particular object retrieval with integral max-pooling of CNN activations
siaMAC+QE*	78.3%	CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
R-MAC	75.7%	Particular object retrieval with integral max-pooling of CNN activations

0 of 7 row(s) selected.

Image Retrieval On Par106K

Metrics

mAP

Results

Performance results of various models on this benchmark

		Paper Title
Offline Diffusion	96.2%	Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
DELF+FT+ATT+DIR+QE	92.8%	Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*	90.5%	Deep Image Retrieval: Learning global representations for image search
DELF+FT+ATT	81.7%	Large-Scale Image Retrieval with Attentive Deep Local Features
R-MAC+R+QE	79.8%	Particular object retrieval with integral max-pooling of CNN activations
siaMAC+QE*	78.3%	CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
R-MAC	75.7%	Particular object retrieval with integral max-pooling of CNN activations

0 of 7 row(s) selected.

Image Retrieval On Par106K | SOTA | HyperAI