Code repository for our paper entilted "Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection" accepted at TCSVT 2024.

arXiv version: https://arxiv.org/abs/2406.01127.

24.7.19. The prediction results and weights based on VGG and ResNet backbones have been updated in the Baidu network disk link below.

If you think our work is helpful, please cite

@article{wang2024learning,
  title={Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection},
  author={Wang, Kunpeng and Tu, Zhengzheng and Li, Chenglong and Zhang, Cheng and Luo, Bin},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2024},
  publisher={IEEE}
}

RGB-D and RGB-T SOD datasets can be found here. [baidu pan fetch code: chjo]

Saliency maps can be found here. [baidu pan fetch code: uodf]

Pretrained parameters can be found here.[baidu pan fetch code: 3ed6]

1. Create directories for the experiment and parameter files.
2. Please use conda to install torch (1.12.0) and torchvision (0.13.0).
3. Install other packages: pip install -r requirements.txt.
4. Set your path of all datasets in ./Code/utils/options.py.

python train.py

python test_produce_maps.py

If you have any questions, please contact us ([email protected]).