Hybrid-Label-SOD_TCSVT2022

Runmin Cong, Qi Qin, Chen Zhang, Qiuping Jiang, Shiqi Wang, Yao Zhao, and Sam Kwong, A weakly supervised learning framework for salient object detection via hybrid labels, IEEE Transactions on Circuits and Systems for Video Technology, 2022. In Press.

Network

Our overall framework(b)：

Refine Network

Requirement

Pleasure configure the environment according to the given version:

• python 3.8.5
• pytorch 1.8.0
• cudatoolkit 11.7
• torchvision 0.9.0
• tensorboardx 2.5.0
• opencv-python 4.4.0.46
• numpy 1.19.2
• timm 0.6.11

We also provide ".yaml" files for conda environment configuration, you can download it from [Link], code: mvpl, then use conda env create -f requirement.yaml to create a required environment.

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
├── data_tset
├── lib
    ├── origin
    ├── CEL.py
    ├── data_prefetcher.py
    ├── LR_Scheduler.py
├── GCF.py
├── net.py
├── 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.

The trained model for S-Net can be download here: [Link], code: mvpl.

python test.py ours\state_final.pt

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

Evaluation

We implement three metrics: MAE(Mean Absolute Error), F-Measure, S-Measure. 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 Trans. Circuits Syst. Video Technol. },
     year={early access, doi: 10.1109/TCSVT.2022.3205182},
     publisher={IEEE}
    }
  

Contact Us

If you have any questions, please contact Runmin Cong at [email protected] or Qi Qin at [email protected].