nnizhang/VST

source code of “Visual Saliency Transformer” (ICCV2021)

Visual Saliency Transformer (VST)

source code for our ICCV 2021 paper “Visual Saliency Transformer” by Nian Liu, Ni Zhang, Kaiyuan Wan, Junwei Han, and Ling Shao.

created by Ni Zhang, email: nnizhang.1995@gmail.com

Requirement

1. Pytorch 1.6.0
2. Torchvison 0.7.0

RGB VST for RGB Salient Object Detection

Data Preparation

Training Set

We use the training set of DUTS to train our VST for RGB SOD. Besides, we follow Egnet to generate contour maps of DUTS trainset for training. You can directly download the generated contour maps DUTS-TR-Contour from [baidu pan fetch code: ow76 | Google drive] and put it into RGB_VST/Data folder.

Testing Set

We use the testing set of DUTS, ECSSD, HKU-IS, PASCAL-S, DUT-O, and SOD to test our VST. After Downloading, put them into RGB_VST/Data folder.

Your RGB_VST/Data folder should look like this:

-- Data
   |-- DUTS
   |   |-- DUTS-TR
   |   |-- | DUTS-TR-Image
   |   |-- | DUTS-TR-Mask
   |   |-- | DUTS-TR-Contour
   |   |-- DUTS-TE
   |   |-- | DUTS-TE-Image
   |   |-- | DUTS-TE-Mask
   |-- ECSSD
   |   |--images
   |   |--GT
   ...

Training, Testing, and Evaluation

1. cd RGB_VST
2. Download the pretrained T2T-ViT_t-14 model [baidu pan fetch code: 2u34 | Google drive] and put it into pretrained_model/ folder.
3. Run python train_test_eval.py --Training True --Testing True --Evaluation True for training, testing, and evaluation. The predictions will be in preds/ folder and the evaluation results will be in result.txt file.

Testing on Our Pretrained RGB VST Model

1. cd RGB_VST
2. Download our pretrained RGB_VST.pth[baidu pan fetch code: pe54 | Google drive] and then put it in checkpoint/ folder.
3. Run python train_test_eval.py --Testing True --Evaluation True for testing and evaluation. The predictions will be in preds/ folder and the evaluation results will be in result.txt file.

Our saliency maps can be downloaded from [baidu pan fetch code: 92t0 | Google drive].

SOTA Saliency Maps for Comparison

The saliency maps of the state-of-the-art methods in our paper can be downloaded from [baidu pan fetch code: de4k | Google drive].

RGB-D VST for RGB-D Salient Object Detection

Data Preparation

Training Set

We use 1,485 images from NJUD, 700 images from NLPR, and 800 images from DUTLF-Depth to train our VST for RGB-D SOD. Besides, we follow Egnet to generate corresponding contour maps for training. You can directly download the whole training set from here [baidu pan fetch code: 7vsw | Google drive] and put it into RGBD_VST/Data folder.

Testing Set

NJUD [baidu pan fetch code: 7mrn | Google drive]
NLPR [baidu pan fetch code: tqqm | Google drive]
DUTLF-Depth [baidu pan fetch code: 9jac | Google drive]
STERE [baidu pan fetch code: 93hl | Google drive]
LFSD [baidu pan fetch code: l2g4 | Google drive]
RGBD135 [baidu pan fetch code: apzb | Google drive]
SSD [baidu pan fetch code: j3v0 | Google drive]
SIP [baidu pan fetch code: q0j5 | Google drive]
ReDWeb-S

After Downloading, put them into RGBD_VST/Data folder.

Your RGBD_VST/Data folder should look like this:

-- Data
   |-- NJUD
   |   |-- trainset
   |   |-- | RGB
   |   |-- | depth
   |   |-- | GT
   |   |-- | contour
   |   |-- testset
   |   |-- | RGB
   |   |-- | depth
   |   |-- | GT
   |-- STERE
   |   |-- RGB
   |   |-- depth
   |   |-- GT
   ...

Training, Testing, and Evaluation

1. cd RGBD_VST
2. Download the pretrained T2T-ViT_t-14 model [baidu pan fetch code: 2u34 | Google drive] and put it into pretrained_model/ folder.
3. Run python train_test_eval.py --Training True --Testing True --Evaluation True for training, testing, and evaluation. The predictions will be in preds/ folder and the evaluation results will be in result.txt file.

Testing on Our Pretrained RGB-D VST Model

1. cd RGBD_VST
2. Download our pretrained RGBD_VST.pth[baidu pan fetch code: zt0v | Google drive] and then put it in checkpoint/ folder.
3. Run python train_test_eval.py --Testing True --Evaluation True for testing and evaluation. The predictions will be in preds/ folder and the evaluation results will be in result.txt file.

Our saliency maps can be downloaded from [baidu pan fetch code: jovk | Google drive].

SOTA Saliency Maps for Comparison

The saliency maps of the state-of-the-art methods in our paper can be downloaded from [baidu pan fetch code: i1we | Google drive].

Acknowledgement

We thank the authors of Egnet for providing codes of generating contour maps. We also thank Zhao Zhang for providing the efficient evaluation tool.

Citation

If you think our work is helpful, please cite

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Nian and Zhang, Ni and Wan, Kaiyuan and Shao, Ling and Han, Junwei},
    title     = {Visual Saliency Transformer},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4722-4732}
}