VSA4VQA

Official code for VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images published at CogSci'24

Installation

# create environment
conda create -n ssp_env python=3.9 pip
conda activate ssp_env
conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia -y

sudo apt install libmysqlclient-dev

# install requirements
pip install -r requirements.txt

# install CLIP 
pip install git+https://github.com/openai/CLIP.git

# setup jupyter notebook kernel
python -m ipykernel install --user --name=ssp_env

Get GQA Programs

using code by https://github.com/wenhuchen/Meta-Module-Network

• Download github repo MMN
• Add gqa-questions folder with GQA json files
• Run Preprocessing
  python preprocess.py create_balanced_programs
• Save generated programs to data folder:

testdev_balanced_inputs.json
trainval_balanced_inputs.json
testdev_balanced_programs.json  
trainval_balanced_programs.json

GQA dictionaries: gqa_all_attributes.json and gqa_all_vocab_classes are also adapted from https://github.com/wenhuchen/Meta-Module-Network

Generate Query Masks

• generates full_relations_df.pkl if not already present
• generates query masks for all relations with more than 1000 samples

python generate_query_masks.py 

Pipeline

Execute Pipeline for all samples in GQA: train_balanced (with TEST=False) or validation_balanced (with TEST=True)

python run_programs.py 

For visualizing samples see code/GQA_PIPELINE.ipynb
For generating figures see code/GQA_EVAL.ipynb

Citation

Please consider citing this paper if you use VSA4VQA or parts of this publication in your research:

@inproceedings{penzkofer24_cogsci,
  author = {Penzkofer, Anna and Shi, Lei and Bulling, Andreas},
  title = {VSA4VQA: Scaling A Vector Symbolic Architecture To Visual Question Answering on Natural Images},
  booktitle = {Proc. 46th Annual Meeting of the Cognitive Science Society (CogSci)},
  year = {2024},
  pages = {}
}