https://github.com/jfkuang/CFAM

Contrast-guided Feature Adjustment Module for Visual Information Extraction
https://github.com/jfkuang/CFAM

Last synced: 2 months ago
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Contrast-guided Feature Adjustment Module for Visual Information Extraction

• Host: GitHub
• URL: https://github.com/jfkuang/CFAM
• Owner: jfkuang
• Created: 2022-11-26T13:23:34.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-05-23T07:24:05.000Z (over 1 year ago)
• Last Synced: 2024-06-17T09:33:53.907Z (4 months ago)
• Language: Python
• Size: 8.57 MB
• Stars: 25
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-key-information-extraction - [link - b08XW_GfAGfPmmII-GDYs/view?usp=share_link) | (Datasets)

README

        
Code and dataset for Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution. (ICDAR2023)

POIE dataset is available at https://drive.google.com/file/d/1eEMNiVeLlD-b08XW_GfAGfPmmII-GDYs/view?usp=share_link.

More details on the code and dataset will be refined soon. Thank you for your attention.

## Introduction

This project is about a novel end-to-end framework with a plug-and-play CFAM for VIE tasks, which adopts contrastive learning and properly designs the representation of VIE tasks for contrastive learning.  

The main branch works with **PyTorch 1.6+**.

### Major Features

## Installation

MMOCR depends on [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection).

Below are quick steps for installation.

Please refer to [Install Guide](https://mmocr.readthedocs.io/en/dev-1.x/get_started/install.html) for more detailed instruction.

```shell

conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y

conda activate open-mmlab

pip3 install openmim

git clone https://github.com/jfkuang/CFAM.git

cd mmocr

mim install -e .

```

## Acknowledgement

We appreciate MMOCR as our codebase.

## Citation

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

```bibtex

@article{

    title={Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution},

    author={Jianfeng Kuang, Wei Hua, Dingkang Liang, Mingkun Yang, Deqiang Jiang, Bo Ren, Yu Zhou, Xiang Bai},

    journal= {The 17th International Conference on Document Analysis and Recognition},

    year={2023}

}