Cream🍦: Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models

Official Implementation of Cream | Paper | Data | Slide | Poster

Introduction

Cream (Contrastive Reading Model) is a language-image understanding module designed to enhance the visually-situated natural language understanding capability in Large Language Models (LLMs). The primary goal of Cream is to effectively capture intricate details in images (e.g., texts), ensuring accurate responses in various visual langauge precessing applications.

Our academic paper, which describes our method in detail and provides full experimental results and analyses, can be found here:

Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models.
Geewook Kim, Hodong Lee, Daehee Kim, Haeji Jung, Sanghee Park, Yoonsik Kim, Sangdoo Yun, Taeho Kil, Bado Lee, Seunghyun Park. In EMNLP 2023.

Updates

2024-01-16 Fix minor errors/typos. Release the PyPi package (pip install cream-python). Further updates will follow shortly.

2023-12-06 First commit with a codebase.

Software Installation

pip install cream-python

or clone this repository and install the dependencies:

git clone https://github.com/naver-ai/cream.git
cd cream/
conda create -n cream_official python=3.8
conda activate cream_official
pip install .

If you want to run train.py or test.py, please also install other dependencies:

pip install -r requirements.txt

Citation

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

@inproceedings{kim2023cream,
      title={Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models}, 
      author={Geewook Kim and Hodong Lee and Daehee Kim and Haeji Jung and Sanghee Park and Yoonsik Kim and Sangdoo Yun and Taeho Kil and Bado Lee and Seunghyun Park},
      booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
      year={2023},
}

License

MIT license

Copyright (c) 2023-present NAVER Cloud Corp.

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