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We provide a pretrained-model for the above setting. You can download it from here.
The fusion process is implemented in test.py, you can turn it on in test.sh. You can turn it on by setting fuse=True. The option eigen_path is used to indicate the directory of eigen vectors. The process to generate eigen vectors is described in spectral.
Citation
If you find SCCNet useful in your research or applications, please cite using this BibTeX:
@inproceedings{wang2023self,
title={Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation},
author={Wang, Linhan and Lei, Shuo and He, Jianfeng and Wang, Shengkun and Zhang, Min and Lu, Chang-Tien},
booktitle={Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
pages={1--10},
year={2023}
}
Acknowledgements
We borrow code from public projects SDM, HSNet, dss.
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Official pytorch implementation of Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation