Bilateral Attention Network for RGB-D Salient Object Detection

Published in IEEE Transactions on Image Processing (TIP)


[Paper 📄]
[ArXiv 🌐]

[Homepage 🏠] »

Prerequisites

Environments

• PyTorch >= 1.0
• Ubuntu 18.04

Usage

1. Download the model parameters and datasets
2. Configure test.sh

--backbones vgg16+vgg11+res50+res2_50 (Multiple items are connected with '+')
--datasets dataset1+dataset2+dataset3
--param_root param (pretrained model path)
--input_root your_data_root (categorize by subfolders)
--save_root your_output_root

1. Run by

   sh test.sh

   Model parameters and prediction results " class="reference-link">Model parameters and prediction results

   | | Model parameters | Prediction results |
   | —— | —— | —— |
   | VGG-16 | [Google Drive] [Baidu Pan (bfrn)] | [Google Drive] [Baidu Pan (k01w)] |
   | VGG-11 | [Google Drive] [Baidu Pan (2a5c)] | [Google Drive] [Baidu Pan (d0t7)] |
   | ResNet-50 | [Google Drive] [Baidu Pan (o9l2)] | [Google Drive] [Baidu Pan (dqw1)] |
   | Res2Net-50 | [Google Drive] [Baidu Pan (k761)] | [Google Drive] [Baidu Pan (h3t9)] |

Citation

@article{zhang2020bianet,
  title={Bilateral attention network for rgb-d salient object detection},
  author={Zhang, Zhao and Lin, Zheng and Xu, Jun and Jin, Wenda and Lu, Shao-Ping and Fan, Deng-Ping},
  journal={IEEE Transactions on Image Processing (TIP)},
  volume={30},
  pages={1949-1961},
  doi={10.1109/TIP.2021.3049959},
  year={2021},
}

Contact

If you have any questions, feel free to contact me via zzhang🥳mail😲nankai😲edu😲cn