README.md

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Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation

[ICML2024] Official Code of Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation

Lincan Cai, Shuang Li, Wenxuan Ma, Jingxuan Kang, Binhui Xie, Zixun Sun and Chengwei Zhu

Contribution

• We gradually constructs intermediate modalities from the source modality to the target modality, bridging the modality gap.
• By mixing the source modality data with the target modality data to construct intermediate modality data, we can also alleviates the issue of insufficient data volume in the target modality.
• Utilize Curriculum Learning, allowing the model to transition from intermediate modality data that is closer to the source modality to that is closer to the target modality. This enables a gradual transfer from easy to difficult tasks.

Requirements

• Please refer to the Requirements of ORCA.
• Download required datasets and precomputed language features text_xs.py and text_ys.py to ./src/datasets when using RoBERTa models for 1D datasets.

Experiment with NAS-Bench-360

Run the following command:

bash run_PaRe.sh

Citation

If you find this project useful in your research, please consider citing:

@inproceedings{caienhancing,
  title={Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation},
  author={Cai, Lincan and Li, Shuang and Ma, Wenxuan and Kang, Jingxuan and Xie, Binhui and Sun, Zixun and Zhu, Chengwei},
  booktitle={Forty-first International Conference on Machine Learning}
}

Acknowledgements

This project is based on the project: ORCA. We thank the authors for making the source code publicly available.