Visual Question Answering

The proposed visual question answering system is based on the variational auto-encoders architecture and designed so that it can take a radiology image and a question as input and generate an answer as output.

gensim==3.0.0
nltk==3.4.5
numpy==1.12.1
Pillow==6.2.0
progressbar2==3.34.3
h5py==2.8.0
torch==0.4.0
torchvision==0.2.0
torchtext==0.2.3
jupyter==1.0.0

install Python requirements:

pip install -r requirements.txt

Once you clone this repo, run the vocab.py, store_dataset.py, train.py and evaluate.py file to process the dataset, to train and evaluate the model.

$ python vocab.py
$ python store_dataset.py
$ python train_vqa.py
$ python evaluate_vqa.py

If you are using this repository or a part of it, please cite our paper:

@inproceedings{sarrouti2020nlm,
  title={NLM at VQA-Med 2020: Visual question answering and generation in the medical domain},
  author={Sarrouti, Mourad},
  year={2020},
  organization={CLEF}
}

For more information, please contact me on sarrouti.mourad[at]gmail.com.