MovieQA: Understanding Stories in Movies through Question-Answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016.
Project page | arXiv preprint | Read the paper | Explore the data

The data is made available in simple JSON / text files for easy access in any environment. We provide Python scripts to help you get started by providing simple data loaders.

To obtain access to the stories, and evaluate new approaches on the test data, please register at our benchmark website.

import MovieQA
mqa = MovieQA.DataLoader()

Movies are indexed using their corresponding IMDb keys. For example
mqa.pprint_movie(mqa.movies_map['tt0133093'])

QAs are stored as a standard Python list mqa.pprint_qa(mqa.qa_list[0])

To get train or test splits of the QA along with a particular story, use
story, qa = mqa.get_story_qa_data('train', 'plot')

Currently supported story forms are: plot, split plot

• numpy
• pysrt