backseason / DFI

Code for our IEEE TIP 2020 paper "Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton"

Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton

This is a demo PyTorch implementation of our IEEE TIP 2020 paper.

We also provide an Online Demo.

Prerequisites

• Pytorch 0.4.1+
• torchvision
• opencv

Demo usage

1. Clone the repository

git clone https://github.com/backseason/DFI.git
cd DFI/

2. Download the pretrained model

dfi.pth GoogleDrive | BaiduYun (pwd: wkeb) and move it to the pretrained folder.

3. Test (demo)

The source images are in the demo/images folder. By running

python main.py

you'll get the predictions under the demo/predictions folder. The predictions of all the three tasks are performed simultaneously.

4. Pre-computed results and evaluation results

You can find the pre-computed predictions maps of all the three tasks and their corresponding evaluation scores with the following link: Results reported in the paper GoogleDrive | BaiduYun (pwd: 7eg3)

5. Contact

If you have any questions, feel free to contact me via: j04.liu(at)gmail.com.

If you think this work is helpful, please cite

@article{liu2020dynamic,
  title={Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton},
  author={Jiang-Jiang Liu and Qibin Hou and Ming-Ming Cheng},
  journal={IEEE Transactions on Image Processing},
  year={2020},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TIP.2020.3017352},
}