Pyramid Contract-based Network for RGB-T Salient Object Detection

Figure.1 The overall architecture of the proposed PCNet model.
The paper can be downloaded from here[code:NEPU], which is accepted in Multimedia Tools and Appllications 🎆.

1.Requirements

Python v3.6, Pytorch 0.4.0+, Cuda 10.0, TensorboardX 2.0, opencv-python

2.Data Preparation

Download the dataset from here[code:NEPU], which includes train, test in train and test dataset. Then put them under the following directory:

-Dataset\   
   -train\  
   -test\ 
       -VT821\
       -VT1000\
       -VT5000_test\
   -test_in_train\

3.Training/Testing & Evaluating

• Training the PCNet

Please download the released code and then:

run python Train.py  

• Testing the PCNet

Please download the trained weights from here and put it to pre folder. Then:

run python Test.py  

Then the test maps will be saved to './salmap/'

• Evaluate the result maps

You can evaluate the result maps using the tool from here[code:NEPU], thanks for Dengpin Fan.

4.Results

• Qualitative comparison

Figure.2 Qualitative comparison of our proposed method with some SOTA methods.

• Quantitative comparison

Table.1 Quantitative comparison with some SOTA models on there public RGB-T SOD benchmark datasets.

• Salmaps
  The results of three RGB-T SOD benchmark datasets can be download from here [code:NEPU]

5.Contact

If you have any questions, feel free to contact us via [email protected] (Ranwan Wu). Please cite: { Wu, R., Bi, H., Zhang, C. et al. Pyramid contract-based network for RGB-T salient object detection. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-15794-z }