Image Retrieval On Par6K

Metrics

mAP

Results

Performance results of various models on this benchmark

		Paper Title
Offline Diffusion	97.8%	Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
DELF+FT+ATT+DIR+QE	95.7%	Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*	93.8%	Deep Image Retrieval: Learning global representations for image search
R-MAC+R+QE	86.5%	Particular object retrieval with integral max-pooling of CNN activations
siaMAC+QE*	85.6%	CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
DELF+FT+ATT	85.0%	Large-Scale Image Retrieval with Attentive Deep Local Features
R-MAC	83.0%	Particular object retrieval with integral max-pooling of CNN activations

0 of 7 row(s) selected.

Image Retrieval On Par6K

Metrics

mAP

Results

Performance results of various models on this benchmark

		Paper Title
Offline Diffusion	97.8%	Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
DELF+FT+ATT+DIR+QE	95.7%	Large-Scale Image Retrieval with Attentive Deep Local Features
DIR+QE*	93.8%	Deep Image Retrieval: Learning global representations for image search
R-MAC+R+QE	86.5%	Particular object retrieval with integral max-pooling of CNN activations
siaMAC+QE*	85.6%	CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
DELF+FT+ATT	85.0%	Large-Scale Image Retrieval with Attentive Deep Local Features
R-MAC	83.0%	Particular object retrieval with integral max-pooling of CNN activations

0 of 7 row(s) selected.

Image Retrieval On Par6K | SOTA | HyperAI