For visual commonsense model. Paper: https://arxiv.org/pdf/1811.10830.pdf

Note that this is unofficial implementation.

1. I am using pytorch pretrained bert from huggingface here: https://github.com/huggingface/pytorch-pretrained-BERT. So that needs to be installed first.
2. Download data from http://visualcommonsense.com/download.html. You should have two folders - images and annotations.
3. cd code
4. Change the cfg.json file to set the vcr_tdir
5. python bert_main.py "some_unique_id" --task_type $T. The task type can be QA or QA_R. Q_AR is under progress.

QA gives 59% on validation set, QA_R gives 66%, both of which are higher than what is reported in the paper (53% and 64%). I am not sure why this the case though. Any inputs are welcome.