Source code for the Paper: "To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection. "
Jian Wang, Miao Zhang, Yongri Piao, ZhengXuan Ma and HuChuan Lu. IIAU-OIP Lab.
The paper is under review, we will release the PDF upon accepted.
- CUDA 10.1
- pytorch 1.7.1
- python 3.7.11
- the others can be found in requirements.txt
our proposed DUTS-Cls dataset can be found in here. code: gpt7
DUTS dataset can be found in here, noting that as a weakly-supervised work, our method only use it's RGB images.
puts the two above datasets in .data/
, named as .DUTScls-44/
and .DUTS-train/image/
, respectively.
link: https://pan.baidu.com/s/1PBzDP1Hnf3RIvpARmxn2yA. code: oipw
Run main.py
here you can adjust the training schedule (such as: learning rate, batchsize, the epoch number of each stage, etc.) in this file.
Run test_code.py
configure the --test_root
as the path of your targeted testset.
the evaluation code can be found in here.
link: https://pan.baidu.com/s/1neboLDAs55DHsmsEO4mv4w. code: rvb0
link: https://pan.baidu.com/s/1oywOIqKPMRQrfogNsMXcrA. code: kuih
Thanks to pioneering helpful works:
- SSSS: Single-stage Semantic Segmentation from Image Labels, CVPR2020, by Nikita Araslanov et al.
- IRNet: Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR2019, by Jiwoon Ahn et al.
We really hope this repo can contribute the conmunity, and if you find this work useful, please use the following citation:
@article{Wang_SCW,
author = {Jian Wang, Miao Zhang, Yongri Piao, Zhengxuan Ma and Huchuan Lu},
title = {To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection},
year = {2021},
url = {https://arxiv.org/abs/2109.01770},
eprinttype = {arXiv},
eprint = {2109.01770},
}
If you have any questions, please contact me by e-mail: [email protected].