Awesome
Noise-Sensitive Adversarial Learning for Weakly Supervised Salient Object Detection
Accepted paper in IEEE Trans on Multimedia, 'Noise-Sensitive Adversarial Learning for Weakly Supervised Salient Object Detection', Yongri Piao, Wei Wu, Miao Zhang, Yongyao Jiang and Huchuan Lu.
Prerequisites
Environment
- Windows 10
- Torch 1.4.0
- CUDA 11.1
- Python 3.6.5
Training data
link: https://pan.baidu.com/s/1n4YGVRhNabM5td4et9o5sw code: wnvl
Training
1st training stage
Case1 : Update soon
Case2 : We upload our 1st pseudo labels in Training data, you can directly use our offered <stage1_training_map> as pseudo labels for convenience.
2nd training stage
setting the training data to the proper root as follows:
NSALWSS -- datasets -- DUTS_pseudo -- DUTS-TR-Image -- 10553 samples
-- stage1_training_map -- 10553 pseudo labels
-- stage2_training_map -- 10553 pseudo labels (not necessary but we also offered stage2's pseudo labels for convenience)
training
Run train.py
testing
Run test_code.py
You need to configure your desired testset in --test_root
The evaluation code can be found in here.
Saliency maps & Checkpoint
We offer our saliency maps and checkpoints.
Saliency maps
link: https://pan.baidu.com/s/1Dyhy107oQTow1UN1Wg9-KA code: 1kfn
Checkpoints
link: https://pan.baidu.com/s/1aMwHkQb-9C2YmM_P-j8f-A code: 32fi
Contact me
If you have any questions, please contact me: [1157008667@qq.com].
Citation
We really hope this repo can contribute the conmunity, and if you find this work useful, please use the following citation:
@ARTICLE{9716868,
author={Piao, Yongri and Wu, Wei and Zhang, Miao and Jiang, Yongyao and Lu, Huchuan},
journal={IEEE Transactions on Multimedia},
title={Noise-Sensitive Adversarial Learning for Weakly Supervised Salient Object Detection},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TMM.2022.3152567}}