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MoADNet

MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection

This is the official implementation of "MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection" as well as the follow-ups. The paper has been published by IEEE Transactions on Circuits and Systems for Video Technology, 2022. The paper link is here.

Content

• Run MoADNet code
• Saliency maps
• Evaluation tools
• Citation

Run MoADNet code

• Train <br> run python train.py <br> # set '--train-root' to your training dataset folder

• Test <br> run python test.py <br> # set '--test-root' to your test dataset folder <br> # set '--ckpt' as the correct checkpoint <br>

Saliency maps

• The saliency maps can be approached in Baidu Cloud (fetach code is moad). Note that the results provided in paper are the average values after several training times.

Evaluation tools

• The evaluation tools, training and test datasets can be obtained in RGBD-SOD-tools.

Citation

@ARTICLE{jin2022moadnet,
  author={Jin, Xiao and Yi, Kang and Xu, Jing},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection}, 
  year={2022},
  volume={32},
  number={11},
  pages={7632-7645}
}