Runmin Cong, Ning Yang, Chongyi Li, Huazhu Fu, Yao Zhao, Qingming Huang, and Sam Kwong, Global-and-local collaborative learning for co-Salient object detection, IEEE Transactions on Cybernetics, 2022.
Project: https://rmcong.github.io/proj_GLNet.html
- Results:
- We provide the resutls of our GLNet on Cosal2015, iCoseg, and MSRC.
Baidu Cloud: https://pan.baidu.com/s/1sXBc4H3fKK8Y8ceaU4AjSQ Password: 0224
- Pytorch implementation of GLNet
- Pretrained model:
- We provide our testing code. If you test our model, please download the pretrained model, unzip it, and put the checkpoint
model_GLNet.pth
toCheckpoints/trained/
folder and put the pretrained backbonebackbone_v.pth
toCheckpoints/warehouse/
folder. - Pretrained model download:
- We provide our testing code. If you test our model, please download the pretrained model, unzip it, and put the checkpoint
Baidu Cloud: https://pan.baidu.com/s/1sXBc4H3fKK8Y8ceaU4AjSQ Password: 0224
- Python 3.7
- Pytorch 1.5.1
- torchvision
- We resize the images of original test datasets. Please download the resized data, and put the data to
Data/
folder. - Resized test datasets:
Baidu Cloud: https://pan.baidu.com/s/1sXBc4H3fKK8Y8ceaU4AjSQ Password: 0224
python test.py
- You can find the results in the
'Outputs/'
folder.
@article{GLNet,
title={Global-and-local collaborative learning for co-Salient object detection},
author={Cong, Runmin and Yang, Ning and Li, Chongyi and Fu, Huazhu and Zhao, Yao and Huang, Qingming and Kwong, Sam},
journal={IEEE Trans. Cybern.},
year={early access, doi: 10.1109/TCYB.2022.3169431},
publisher={IEEE}
}
If you have any questions, please contact Runmin Cong (rmcong@bjtu.edu.cn) or Ning Yang (ningyang@bjtu.edu.cn).