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Aggregating Attentional Dilated Features for Salient Object Detection

by Lei Zhu^, Jiaxing Chen^, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Jing Qin, and Pheng-Ann Heng (^ joint 1st authors)[paper link]

This implementation is written by Jiaxing Chen at the South China University of Technology.

Citation

@article{zhu2019aggregating,
     title={Aggregating Attentional Dilated Features for Salient Object Detection},
     author={Zhu, Lei and Chen, Jiaxing and Hu, Xiaowei and Fu, Chi-Wing and Xu, Xuemiao and Qin, Jing and Heng, Pheng-Ann},
     journal={IEEE Transactions on Circuits and Systems for Video Technology},
     year = {2019},
     publisher={IEEE}
}

Saliency Map

The results of salient object detection on seven datasets (ECSSD, HKU-IS, PASCAL-S, SOD, DUT-OMRON, DUTS-TE, SOC) can be found at Google Drive.

Trained Model

You can download the trained model which is reported in our paper at Google Drive.

Requirement

Training

  1. Set the path of pretrained DenseNet model in densenet/config.py
  2. Set the path of DUTS dataset in config.py
  3. Run by python train.py

Hyper-parameters of training were gathered at the beginning of train.py and you can conveniently change them as you need.

Testing

  1. Set the path of six benchmark datasets in config.py
  2. Put the trained model in ckpt/AADFNet
  3. Run by python infer.py

Settings of testing were gathered at the beginning of infer.py and you can conveniently change them as you need.

Dataset links