Awesome
SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images
Zhishe Wang,Yanlin Chen,Wenyu Shao,Hui Li,Lei Zhang
Platform
Python 3.7
Pytorch >=1.6.0
Training Dataset
MS-COCO 2014 (T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 3-5.) is utilized to train our auto-encoder network.
Tips:<br>
Large files should be downloaded separately, including the following files: <br>
For testing:<br>
- trained model<br>
Extraction code: mn9k
Ciation
If this work is helpful to you, please cite it as:
@ARTICLE{9832006
author={Wang, Zhishe and Chen, Yanlin and Shao, Wenyu and Li, Hui and Zhang, Lei},
journal={IEEE Transactions on Instrumentation and Measurement},
title={SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TIM.2022.3191664}}
If you have any question, please email to me (wangzs@tyust.edu.cn).