C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion (ICLR 2024)

[Paper]

Running Experiments

The code will be updated soon.

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2022-0-00184, Development and Study of AI Technologies to Inexpensively Conform to Evolving Policy on Ethics), and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2022-0-00951, Development of Uncertainty-Aware Agents Learning by Asking Questions).

Citation

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

@inproceedings{
yoon2024ctpt,
title={C-{TPT}: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion},
author={Hee Suk Yoon and Eunseop Yoon and Joshua Tian Jin Tee and Mark A. Hasegawa-Johnson and Yingzhen Li and Chang D. Yoo},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=jzzEHTBFOT}
}

Contact

If you have any questions, please feel free to email hskyoon@kaist.ac.kr