QACE

This repository provides an image captioning metric from our EMNLP-Findings 2021 paper QACE: Asking Questions to Evaluate an Image Caption .

1) Visual-T5 - Abstractive VQA model

0. Detection Feature Extraction

Refer to https://github.com/hwanheelee1993/BUTD-UNITER-NLVR2

1. Install Requirements

python 3.6.6
pip install -r requirements.txt

2. Pretrained model download

https://vqamodel.s3.us-east-2.amazonaws.com/t5vqa/ckpt.zip

unzip the file to "ckpt"

3. Run Demo

Refer to demo.ipynb

2) Computing QACE

Refer to qace_demo.ipynb

Answer similarity computation code will be updated soon. (e.g. using BERTScore)

Reference

@misc{lee2021qace,
      title={QACE: Asking Questions to Evaluate an Image Caption}, 
      author={Hwanhee Lee and Thomas Scialom and Seunghyun Yoon and Franck Dernoncourt and Kyomin Jung},
      year={2021},
      eprint={2108.12560},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}