This is the official PyTorch implementation of our TIP 2021 paper.
git clone https://github.com/zal0302/CII.git
cd CII/Download the following datasets for testing and unzip them into data folder.
Download the following pre-trained models for CII with ResNet50 backbone and ResNet18 backbone into saved/models folder.
For all datasets testing used in our paper for ResNet50 backbone:
python test.py -r saved/models/cii.pth -c saved/models/config.jsonand for ResNet18 backbone:
python test.py -r saved/models/cii_res18.pth -c saved/models/config_resnet18.jsonAll results saliency maps will be stored under saved/results folders in .png formats.
You may refer to this repo for results evaluation: SalMetric.
We provide the pre-computed saliency maps and evaluation results for ResNet50 backbone and ResNet18 backbone.
If you have any questions, feel free to contact me via: liuzhiang(at)mail.nankai.edu.cn.
@article{liu2021rethinking,
title={Rethinking the U-Shape Structure for Salient Object Detection},
author={Liu, Jiang-Jiang and Liu, Zhi-Ang and Peng, Pai and Cheng, Ming-Ming},
journal={IEEE Transactions on Image Processing},
volume={30},
pages={9030--9042},
year={2021},
publisher={IEEE}
}@article{liu2022poolnet+,
title={Poolnet+: Exploring the potential of pooling for salient object detection},
author={Liu, Jiang-Jiang and Hou, Qibin and Liu, Zhi-Ang and Cheng, Ming-Ming},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}
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