Code for the paper is coming soon
clone repo:
git clone https://github.com/zengxianyu/jsws-old.git
git submodule init
git submodule update
prepare environment:
conda env create --file=pytorch_environments.yml
./trainaug/txt DUTS-train (Onedrive)
SegmentationClassAug (Onedrive)
or
./SegmentationClassAug
train using image-level class labels and saliency ground-truth:
weak_seg_full_sal_train.py
Open http://host ip:8000/savefiles/jlsfcn_dense169.html in a browser for visualizing training process.
It should be easy to achieve MIOU>54 but you may need to try multiple times to get the score MIOU 57.1 or more than that in Table. 5 of the paper.
train a more complex model using the prediction of the model trained in the stage 1.
weak_seg_full_sal_syn.py
self_seg_full_sal_train.py
stage 1 model:
weak_seg_full_sal_test.py
stage 2 model
self_seg_full_sal_test.py
By default it calls the function test(...)
to test on segmentation task
Change to call the function test_sal(...)
to test on saliency task
download saliency maps on datasets ECSSD, PSACALS, HKU-IS, DUT-OMRON, DUTS-test, SOD; Google Drive; One Drive
@inproceedings{zeng2019joint,
title={Joint learning of saliency detection and weakly supervised semantic segmentation},
author={Zeng, Yu and Zhuge, Yunzhi and Lu, Huchuan and Zhang, Lihe},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
year={2019}
}