DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

This repo is the official implementation of "DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion"

by Peng Sun, Wenhu Zhang, Huanyu Wang, Songyuan Li, and Xi Li.

Prerequisites

• Ubuntu 18
• PyTorch 1.7.0
• CUDA 10.1
• Cudnn 7.5.1
• Python 3.7
• Numpy 1.17.3

Training

Please see launch_train.sh and launch_pretrain.sh for imagenet pretraining and sod training, respectively.

Testing

Please see launch_test.sh for testing on the sod benchmarks.

Main Results

Saliency maps and Evaluation

All of the saliency maps mentioned in the paper are available on GoogleDrive or BaiduYun(code:juc2).

You can use the toolbox provided by jiwei0921 for evaluation.

Additionally, we also provide the saliency maps of the STERE-1000 and SIP dataset on BaiduYun(code:qxfw) for easy comparison.

Citation

@inproceedings{Sun2021DeepRS,
  title={Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion},
  author={P. Sun and Wenhu Zhang and Huanyu Wang and Songyuan Li and Xi Li},
  journal={IEEE Conf. Comput. Vis. Pattern Recog.},
  year={2021}
}

License

The code is released under MIT License (see LICENSE file for details).