This repo contains the codebase of the ICCV'23 paper Bayesian Prompt Learning for Image-Language Model Generalization.
This code is built on top of the awesome toolbox Dassl.pytorch so you need to install the dassl environment first. Simply follow the instructions described here to install dassl as well as PyTorch. After that, run pip install -r requirements.txt under VPT/ to install a few more packages required by CLIP (this should be done when dassl is activated). Then, you are ready to go.
In this paper, we follow DATASETS.md to install the datasets. The task definition and few-shot learning setting are similar to following papers for fair comparison:
- Conditional Prompt Learning for Vision-Language Models, in CVPR, 2022.
- Learning to Prompt for Vision-Language Models, in IJCV, 2022.
Click a paper below to see the detailed instructions on how to run the code to reproduce the results.
The raw numerical results can be found at this google drive link.
If you use this code in your research, please kindly cite the following papers
@article{derakhshani2023variational,
title={Bayesian Prompt Learning for Image-Language Model Generalization},
author={Derakhshani, Mohammad Mahdi and Sanchez, Enrique and Bulat, Adrian and da Costa, Victor Guilherme Turrisi and Snoek, Cees GM and Tzimiropoulos, Georgios and Martinez, Brais},
journal={ICCV},
year={2023}
}