README.md

Active Prompt Learning in Visual Language Models

This repository is the official PyTorch implementation of the following paper.

Active Prompt Learning in Visual Language Models
Jihwan Bang, Sumyeong Ahn, Jae-Gil Lee
CVPR 2024

Updates

• [Jun.17th.2024] First release the code.

How to Install

This code is built on the CoOp repository and it built on top of the awesome toolbox Dassl.pytorch. For simply usage, I add the dassl directory into our directory, and revise requirements.txt to run the code. Hence, you should follow below commands:

conda create -n pcb python=3.10 
conda activate pcb 
cd pcb 
pip install -r requirements.txt 

Next, you should build on the datasets - follow DATASETS.md to install the datasets.

How to Run

To run the code, you need to look into scripts/alvlm/main.sh. In this file, you must set parameter DATA as the directory path that datasets are stored. After then, you can run the code by following command.

CUDA_VISIBLE_DEVICES=XX sh scripts/alvlm/main.sh [DATASET NAME] [MODEL NAME] [AL METHOD] [SEED NUMBER] [MODE] 

• DATASET NAME $\in$ [oxford_flowers, dtd, oxford_pets, caltech101, stanford_cars, eurosat, fgvc_aircraft]
• MODEL NAME $\in$ [RN50, RN101, vit_b32, vit_b16]
• AL METHOD $\in$ [random, entropy, coreset, badge]
• SEED: integer
• MODE: This is for description augmentation $\in$ [none, AS, AE]

Citation

If you use this code in your research, please kindly cite the following papers

@inproceedings{bang2024active,
  title={Active Prompt Learning in Vision Language Models},
  author={Bang, Jihwan and Ahn, Sumyeong and Lee, Jae-Gil},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={27004--27014},
  year={2024}
}