SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection

The paper has been online published by IEEE Transactions on Circuits and Systems for Video Technology.

The details are in http://arxiv.org/abs/2204.05585

DOI: https://dx.doi.org/10.1109/TCSVT.2021.3127149

Revision：paper

Training Set

2185 https://drive.google.com/file/d/1fcJj4aYdJ6N-TvvxSZ_sBo-xhtd_w-eJ/view?usp=sharing

2985 https://drive.google.com/file/d/1mYjaT_FTlY4atd-c0WdQ-0beZIpf8fgh/view?usp=sharing

Testing Set

Google https://drive.google.com/file/d/1dQWrhO-LHVACDKCTrvdc6eIbcyH7Yzp_/view?usp=sharing

Baidu：https://pan.baidu.com/s/1fN-AsBiKTbMRsye0Lr8K7g 提取码：l2u9

Pretraining Parameter

Swin Transformer Parameter: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth

Our RGB-D SOD Parameters:

Google: https://drive.google.com/file/d/1amFbdnGefUaej1NMb2EOVgsMoQ4zabe_/view?usp=sharing

Baidu：https://pan.baidu.com/s/1ouefnVNRfUwck3zNV9J2Dw 提取码：c4ix

Our RGB-T SOD Parameter:

Google： https://drive.google.com/file/d/1rD19-p7M2bv0btjZ11DoZZ3zY9qxXp03/view?usp=sharing

Baidu：https://pan.baidu.com/s/1xuDNtr3x-Foi-o9QaYMWuw 提取码：nqh2

RGBD Result Saliency Maps

Google: https://drive.google.com/file/d/1tiHhSPocBZR3rO6XxIZm3ICHMQxRngci/view?usp=sharing

Baidu：https://pan.baidu.com/s/1jGL47H9hucbTxrNvj2uRXw 提取码：4gas

RGBT Result Saliency Maps

Google: https://drive.google.com/file/d/1r072vcAtA7UHprRo1K1clLEZzDYAXCKc/view?usp=sharing

Baidu：https://pan.baidu.com/s/1nneg3IIHi62NebLdyI7M-w 提取码：9pde

Evaluation Code

Google: https://drive.google.com/file/d/1-fXbORnXNVfvm7uarXLX6w9PuVhs-25d/view?usp=sharing

Baidu：https://pan.baidu.com/s/1EtKTNVPQAW_iLm8pA1POmQ 提取码：86gt

Citation

If you find the information useful, please consider citing:

@article{liu2022,
  author={Liu, Zhengyi and Tan, Yacheng and He, Qian and Xiao, Yun},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection}, 
  volume={32},
  number={7},
  pages={4486--4497},
  year={2022}}

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