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Self-PT: Adaptive Self-Prompt Tuning for Low-Resource Visual Question Answering (ACM MM' 23)

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Self-PT: Adaptive Self-Prompt Tuning for Low-Resource Visual Question Answering

Official code and models for the ACM MM 2023 paper:

Self-PT: Adaptive Self-Prompt Tuning for Low-Resource Visual Question Answering

Bowen Yuan, Sisi You, Bing-Kun Bao*

ACM Multimedia 2023

Self-PT is a context-aware prompt tuning method for low-resource VQA, which can adapt large vision-language pretraining models to VQA tasks with only ~1M parameters and 16 training samples! If you have any questions, please feel free to raise an issue or email [email protected].

Details can be found in https://github.com/NJUPT-MCC/Self-PT.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{yuan2023self,
  title={Self-PT: Adaptive Self-Prompt Tuning for Low-Resource Visual Question Answering},
  author={Yuan, Bowen and You, Sisi and Bao, Bing-Kun},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  pages={5089--5098},
  year={2023}
}

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Self-PT: Adaptive Self-Prompt Tuning for Low-Resource Visual Question Answering (ACM MM' 23)

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