Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution（ACTR）
This is the official code for ACTR implemented with PyTorch.
git clone https://github.com/YXSUNMADMAX/ACTR
cd ACTR
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -U scikit-image
pip install git+https://github.com/albumentations-team/albumentations
pip install tensorboardX termcolor timm tqdm requests pandas
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Download pre-trained weights on Link
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Result on SPair-71k: python test.py --datapath "/path_to_dataset" --pretrained "/path_to_pretrained_model/spair" --benchmark spair
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Results on PF-PASCAL: python test.py --datapath "/path_to_dataset" --pretrained "/path_to_pretrained_model/pfpascal" --benchmark pfpascal
We borrow code from public projects (Thanks a lot !!!). We mainly borrow code from CATs.
If you find this research useful, please consider citing:
@inproceedings{sun2023correspondence,
title={Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution},
author={Sun, Yixuan and Zhao, Dongyang and Yin, Zhangyue and Huang, Yiwen and Gui, Tao and Zhang, Wenqiang and Ge, Weifeng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={17787--17796},
year={2023}
}
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