GQA, a generative method proposed by Lewis et al., is shown to perform well for image and text data. Our paper aims to extend GQA in 2 ways:

1, Incorporate a generative answer model in for Image-based GQA in order to expand the choices of candidate answers for a question by introducing a new seq2seq model for the answer generator that takes the image and some ‘weak representation’ of the question as inputs.

To view the sub-module and its contents, run:

cd  ImageQA
ls

See README.md for instructions on how to run the model scripts.

2, Apply GQA to video QA on questions that requires spatial and temporal grounding to obtain a relevant answer. The model is evaluated on TVQAplus dataset.

cd  TVQAplus
ls

See README.md for instructions on how to run the model scripts.