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code for the paper ``learning to promote saliency detectors"
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datasets dkgd Nov 8, 2018
models Delete psi.py~ Nov 8, 2018
README.md Update README.md Feb 22, 2019
evaluate.py dgd Nov 8, 2018
test.py dkgd Nov 8, 2018
train.py Update train.py Nov 8, 2018

README.md

lps

code for the paper ``learning to promote saliency detectors"

Environment: python 2.7; pytorch '0.5.0a0+54db14e'; two GTX 1080Ti GPU;

usage

modify the path to prior maps, images, groud truth and then run test.py

pre-trained model

download pre-trained model

performance and results

This version gives slightly different results from the paper.

Prior maps are the results of other methods which are provided by the authors or obtained by runing their code. For example, results of SRM can be downloaded here. Results of applying our method on SRM can be downloaded from 百度网盘 or google drive

F-measure

v ECSSD HKU-IS PASCALS DUTS-test THUR OMRON
SRM .8924 .8739 .7961 .7591 .7079 .7223
+Ours .9102 .9032 .8054 .7999 .7299 .7338

MAE

v ECSSD HKU-IS PASCALS DUTS-test THUR OMRON
SRM .0542 .0459 .0852 .0633 .0769 .0767
+Ours .0416 .0330 .0729 .0536 .0735 .0696

I'll add the results on other methods later.

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