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MovieQA: Understanding Stories in Movies through Question-Answering

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arxiv 1512.02902 v2 pith:YU3EOI5R submitted 2015-12-09 cs.CV cs.CL

Makarand Tapaswi , Yukun Zhu , Rainer Stiefelhagen , Antonio Torralba , Raquel Urtasun , Sanja Fidler This is my paper · ORCID
classification cs.CVcs.CL
keywords datasetanswersdatamovieqamoviesquestion-answeringquestionsvideo
verification ladder T0 review T1 audit T2 compute T3 formal
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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 information -- video clips, plots, subtitles, scripts, and DVS. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data set public along with an evaluation benchmark to encourage inspiring work in this challenging domain.

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Cited by 2 Pith papers

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  2. The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering

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    A survey tracing the evolution of visual question answering from 2015 CNN-LSTM models through attention mechanisms, modular networks, vision-language pretraining, and large multimodal models.

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