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TokenMixers

Introduction

This repository includes the official Pytorch implementation for the following works on the basic neural operaters for mixing tokens, i.e., token mixers:

Citing

If you find this code and work useful, please consider citing the following paper and star this repo. Thank you very much!

@inproceedings{wei2023active,
  title={Active Token Mixer},
  author={Wei, Guoqiang and Zhang, Zhizheng and Lan, Cuiling and Lu, Yan and Chen, Zhibo},
  booktitle={AAAI},
  year={2023}
}

@inproceedings{huang2023adaptive,
  title={Adaptive Frequency Filters As Efficient Global Token Mixers},
  author={Huang, Zhipeng and Zhang, Zhizheng and Lan, Cuiling and Zha, Zheng-Jun and Lu, Yan and Guo, Baining},
  booktitle={ICCV},
  year={2023}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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