WSLNet

This is a Mindspore implementation of "A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels" accepted by IEEE TCSVT 2022.

Paper: https://doi.org/10.1109/TCSVT.2022.3205182.

Arxiv version: https://arxiv.org/pdf/2209.02957.pdf

Network

Our overall framework(b)：

Refine Network

Requirement

Pleasure configure the environment according to the given version:

• python 3.8.5
• mindspore-gpu 1.9.0
• GPU cuda 11.1

Data Preprocessing

Please follow the tips to download the processed datasets and pre-trained model:

1. Download training data from [Link], code: mvpl.
2. Download testing data from [Link], code: mvpl.

├── data
    ├── coarse
    ├── DUTS
    ├── SOD
    ├── dataset.py 
    ├── transform.py
├── Network
    ├── rnet_down
    ├── rnet_up
    ├── snet
├── data_tset
├── test.py
├── train.py

Training and Testing

Training command : Please unzip the training data set to data\DUTS and unzip coarse maps of training data set to data\coarse.

python train.py

Tips: Our validation set is 100 images from the SOD dataset.

Testing command : Please unzip the testing data set to data_test.

python test.py ours\xx.pt

Tips: We use Toolkit [Link] to obtain the test metrics.

Results

1. Qualitative results: we provide the saliency maps, you can download them from [Link], code: 0812.
2. Quantitative results:

Bibtex

@article{HybridSOD,
  title={A weakly supervised learning framework for salient object detection via hybrid labels},
  author={Cong, Runmin and Qin, Qi and Zhang, Chen and Jiang, Qiuping and Wang, Shiqi and Zhao, Yao and Kwong, Sam },
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2022},
  publisher={IEEE}
}
  

Contact Us

If you have any questions, please contact Runmin Cong at rmcong@bjtu.edu.cn or Qi Qin at qiqin96@bjtu.edu.cn.