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Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge

Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge accepted to ACL 2023 Findings.

Prerequisites

How to run the code

To start with,

git clone --recurse-submodules git@github.com:awslabs/vqa-generate-then-select.git
cd vqa-generate-then-select
cp -r src/* KAT
cp -r PICa/* KAT
cd KAT
pip install -r requirements.txt
pip install -r requirements-new.txt
pip install -e .
  1. Candidate generation
python gen_answers.py
  1. Train VQA selector
python build_vqa_input.py

bash train_vqa.sh

References

Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge

@inproceedings{fu-etal-2023-generate,
title = "Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge",
author = "Fu, Xingyu  and
    Zhang, Sheng  and
    Kwon, Gukyeong  and
    Perera, Pramuditha  and
    Zhu, Henghui  and
    Zhang, Yuhao  and
    Li, Alexander Hanbo  and
    Wang, William Yang  and
    Wang, Zhiguo  and
    Castelli, Vittorio  and
    Ng, Patrick  and
    Roth, Dan  and
    Xiang, Bing",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.147" }

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