MovieQA: Understanding Stories in Movies through Question-Answering

@article{Tapaswi2016MovieQAUS,
  title={MovieQA: Understanding Stories in Movies through Question-Answering},
  author={Makarand Tapaswi and Y. Zhu and R. Stiefelhagen and A. Torralba and R. Urtasun and S. Fidler},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={4631-4640}
}
  • Makarand Tapaswi, Y. Zhu, +3 authors S. Fidler
  • Published 2016
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events occurred. Each question comes with a set of five possible answers, a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of… CONTINUE READING
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