FSMINet (GRSL 2022)

'Fully Squeezed Multi-Scale Inference Network for Fast and Accurate Saliency Detection in Optical Remote Sensing Images', Kunye Shen, Xiaofei Zhou, Bin Wan, Ran Shi, Jiyong Zhang.

Required libraries

Python 3.7
numpy 1.18.1
scikit-image 0.17.2
PyTorch 1.4.0
torchvision 0.5.0
glob

The SSIM loss is adapted from pytorch-ssim.

Usage

1. Clone this repo

https://github.com/Kunye-Shen/FSMINet.git

2. We provide the predicted saliency maps (GoogleDrive or baidu extraction code: 12so.). You can download directly through the above methods, or contact us through the following email.

KunyeShen@outlook.com

Architecture

FSM Module

FSMINet

Quantitative Comparison

Qualitative Comparison

Citation

@article{shen2022fully,
  title={Fully Squeezed Multi-Scale Inference Network for Fast and Accurate Saliency Detection in Optical Remote Sensing Images},
  author={Shen, Kunye and Zhou, Xiaofei and Wan, Bin and Shi, Ran and Zhang, Jiyong},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2022},
  publisher={IEEE}
}