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WaveNet

This project provides the code and results for 'WaveNet: Wavelet Network With Knowledge Distillation for RGB-T Salient Object Detection', IEEE TIP, 2023. IEEE link

Requirements

Python 3.7+, Pytorch 1.5.0+, Cuda 10.2+, TensorboardX 2.1, opencv-python, pytorch_wavelets, timm.

Architecture and Details

drawing

Results

drawing

Preparation

  • Download the RGB-T raw data from LSNet.
  • Options: Download the pre-trained wavemlp-s from wavemlp.
  • We have two ways of training knowledge distillation:
    1. Load the SwinNet model, please refer to the specific configuration of SwinNet.
    2. Directly load the prediction maps of SwinNet baidu pin: py5y.
      We use prediction maps of SwinNet as the default setting.

Training & Testing

Modify the train_root train_root save_path path in config.py according to your own data path.

  • Train the WaveNet:

    python train.py

Modify the test_path path in config.py according to your own data path.

  • Test the WaveNet:

    python test.py

Evaluate tools

Saliency Maps

Pretraining Models

Citation

    @ARTICLE{10127616,
        author={Zhou, Wujie and Sun, Fan and Jiang, Qiuping and Cong, Runmin and Hwang, Jenq-Neng},
        journal={IEEE Transactions on Image Processing}, 
        title={WaveNet: Wavelet Network With Knowledge Distillation for RGB-T Salient Object Detection}, 
        year={2023},
        volume={32},
        number={},
        pages={3027-3039},
        doi={10.1109/TIP.2023.3275538}}     

Acknowledgement

The implementation of this project is based on the codebases below.

If you find this project helpful, Please also cite the codebases above. Besides, we also thank zyrant.

Contact

Please drop me an email for any problems or discussion: https://wujiezhou.github.io/ (wujiezhou@163.com).

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