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dc.contributor.authorTapaswi, Makarand
dc.contributor.authorZhu, Yukun
dc.contributor.authorStiefelhagen, Rainer
dc.contributor.authorTorralba, Antonio
dc.contributor.authorUrtasun, Raquel
dc.contributor.authorFidler, Sanja
dc.date.accessioned2018-02-26T21:43:32Z
dc.date.available2018-02-26T21:43:32Z
dc.date.issued2016-12
dc.date.submitted2016-06
dc.identifier.isbn978-1-4673-8851-1
dc.identifier.urihttp://hdl.handle.net/1721.1/113894
dc.description.abstractWe 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. Keywords: Motion pictures, Visualization, Semantics, Voltage control, Cognition, Natural languages, Computer visionen_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/CVPR.2016.501en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleMovieQA: Understanding Stories in Movies through Question-Answeringen_US
dc.typeArticleen_US
dc.identifier.citationTapaswi, Makarand, et al. "MovieQA: Understanding Stories in Movies through Question-Answering." 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June, 2016, Las Vegas, Nevada, IEEE, 2016, pp. 4631–40.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorTorralba, Antonio
dc.relation.journal2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsTapaswi, Makarand; Zhu, Yukun; Stiefelhagen, Rainer; Torralba, Antonio; Urtasun, Raquel; Fidler, Sanjaen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-4915-0256
mit.licenseOPEN_ACCESS_POLICYen_US


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