This code is for the paper "Reverse Attention for Salient Object Detection".pdf
A PyTorch version is available at here.
@inproceedings{chen2018eccv,
author={Chen, Shuhan and Tan, Xiuli and Wang, Ben and Hu, Xuelong},
booktitle={European Conference on Computer Vision},
title={Reverse Attention for Salient Object Detection},
year={2018}
}
@article{chen2020tip,
author={Chen, Shuhan and Tan, Xiuli and Wang, Ben and Lu, Huchuan and Hu, Xuelong and Fu, Yun},
journal={IEEE Transactions on Image Processing},
title={Reverse Attention Based Residual Network for Salient Object Detection},
volume={29},
pages={3763-3776},
year={2020}
}
- Install prerequisites for Caffe (http://caffe.berkeleyvision.org/installation.html#prequequisites).
- Build DSS [1] with cuDNN v5.1 for acceleration. Supposing the root directory of DSS is
$DSS
.
USE_CUDNN := 1
- Copy the folder RAS to
$DSS/example/
.
- Prepare training dataset and its corresponding data list.
- Download the Pre-trained VGG model (VGG-16) and copy it to
$DSS/example/ras
. - Change the dataset path in
$DSS/example/RAS/train.prototxt
. - Run
solve.py
in shell (or you could use IDE like Eclipse).
cd $DSS/example/RAS/
python solve.py
- Change the dataset path in
$DSS/example/RAS-tutorial_save.py
. - Run
jupyter notebook RAS-tutorial_save.ipynb
.
We use the code of [1] for evaluation.
Pre-trained RAS model on MSRA-B: Baidu drive(h7qj) and Google drive.
Note that this released model is newly trained and is slightly different from the one reported in our paper.
ECCV 2018: The saliency maps on 7 datasets are available at Baidu drive(zin5) and Google drive.
TIP 2020: The saliency maps on 6 datasets are available at Google drive.
[1] Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.: Deeply supervised salient object detection with short connections. In: CVPR. (2017) 5300–5309.