Code for the paper in CVPR2019, 'Multi-source weak supervision for saliency detection' (download the pdf file)
score/datasets | ECSSD | HKU-IS | PASCALS | SOD | OMRON | DUTS-test | SED1 | SED2 |
---|---|---|---|---|---|---|---|---|
max$F_\beta$ | .878 | .856 | .790 | .799 | .718 | .767 | .902 | .849 |
MAE | .096 | .084 | .134 | .167 | .114 | .096 | .081 | .097 |
Download result maps: OneDrive / GoogleDrive
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Environment: python2.7, pytorch'1.0'
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Download models and put in the current folder
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Run
python main.py \
--img_dir 'path/to/images(.jpg)' \
--gt_dir 'path/to/ground-truth(.png)'
@inproceedings{zeng2019multi,
title={Multi-source weak supervision for saliency detection},
author={Zeng, Yu and Zhuge, Yunzhi and Lu, Huchuan and Zhang, Lihe and Qian, Mingyang and Yu, Yizhou},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}