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DPANet : Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection

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# DPANet:Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection [(paper)](https://ieeexplore.ieee.org/document/9247470)  [(project)](https://rmcong.github.io/proj_DPANet.html) [(talk)](https://www.bilibili.com/video/BV1Ry4y1m7WL)

This repo. is an official implementation of the *DPANet* , which has been published on the journal *IEEE Transactions on Image Processing, 2021*.

The main pipeline is shown as the following, 
![DPANet](https://raw.githubusercontent.com/JosephChenHub/DPANet/master/figures/TIP_main.png)

And some visualization results are listed 
![vis](https://raw.githubusercontent.com/JosephChenHub/DPANet/master/figures/vis.png)

![gma](https://raw.githubusercontent.com/JosephChenHub/DPANet/master/figures/gma.png)

## Dependencies 
```
>= Pytorch 1.0.0
OpenCV-Python
[optional] matlab
```

## Preparation 
- download the official pretrained model of ResNet-50/ResNet-34/ResNet-18 implemented in Pytorch if you want to train the network again.
- download or put the RGB-D saliency benchmark datasets ([Google drive](https://drive.google.com/file/d/19pvXom9vs7cS4L1y-bZI8YDtSg7r4LT0/view?usp=sharing)) in the folder of `data` for training or test.
- [optional] generate the pseudo label (provided for `NJUD` and `NLPR`) using the scripts `gen.m` and `cal_score.py`.

## Training
```
python3 train.py --tag res50 --save_path res50_res
```
make sure  that the GPU memory is enough (the original training is conducted on 8 NVIDIA RTX2080Ti cards with the batch size of 32).

## Test
```
 python3 test.py --tag res50 --gpu 0 --model res50_res/model-30
```

We provide the trained model file ([Google drive](https://drive.google.com/file/d/1bXERDgTKfzkZfXKs8z5vj1QNM3zL-QTL/view?usp=sharing)), and run this command to check its integrity:
```
md5sum model-res50-epoch30.pt 
```
you will obtain the code `b666d297e0237035f6e48f80711ca927`.
Please use the matlab code to evaluate the MAE, F-measure, or other metrics rather than using the `accuracy` defined in the `test.py`.

The saliency maps are also available ([Google drive](https://drive.google.com/file/d/1sIqEKDCi_rSY4t1THPlBSyAd05F2ve_Q/view?usp=sharing)).

## Evaluation
We provide the evaluation code in the folder  "eval_code" for fair comparisons. You may need to revise the `algorithms` , `prepath`, and `maskpath` defined in the `main.m`. The saliency maps of the competitors (official maps or obtained by running the official code) are provided ([Google drive](https://drive.google.com/file/d/1SEhDaiIVJJccv7dIlpk_rp0KZhWab8ie/view?usp=sharing)).

## Citation
Please cite the `DPANet` in your publications if it helps your research:
```
@article{DPANet,
  title={{DPANet}: Depth potentiality-aware gated attention network for {RGB-D} salient object detection},
  author={Chen, Zuyao and Cong, Runmin and Xu, Qianqian and Huang, Qingming},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  publisher={IEEE}
}
```

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