CATNet: A Cascaded and Aggregated Transformer Network For RGB-D Salient Object Detection

The paper has been online published by IEEE Transactions on Multimedia.

Requirements

python 3.9

pytorch 1.11.0

tensorboardX 2.5

Results and Saliency maps

We provide saliency maps of our CATNet on 7 datasets.

Training

Please run

CatNet_train.py

Pre-trained model and testing

• Download the following pre-trained models (Swin Transformer) and put them in /pre.
• Modify pathes of pre-trained models and datasets.
• Run

CatNet_test.py

Downloading Training and Testing Datasets:

• Download the training set used for training.
• Download the testing sets used for testing.

Citation

If you find this work is useful for your research, please cite our paper:

@article{10179145,
  author={Sun, Fuming and Ren, Peng and Yin, Bowen and Wang, Fasheng and Li, Haojie},
  journal={IEEE Transactions on Multimedia}, 
  title={CATNet: A Cascaded and Aggregated Transformer Network For RGB-D Salient Object Detection}, 
  year={2023},
  volume={},
  number={},
  pages={1-14},
  doi={10.1109/TMM.2023.3294003}}

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