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Story Understanding through Semantic Analysis and Automatic Alignment of Text and Video

Tapaswi, Makarand Murari



Abstract (englisch): Humans spend a large amount of time listening, watching, and reading stories. We argue that the ability to model, analyze, and create new stories is a stepping stone towards strong AI. We thus work on teaching AI to understand stories in films and TV series. To obtain a holistic view of the story, we align videos with plot synopses and books; visualize character interactions as a chart; and finally, test machine understanding of stories by asking it to answer questions.


Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Jahr 2016
Sprache Englisch
Identifikator DOI(KIT): 10.5445/IR/1000056619
URN: urn:nbn:de:swb:90-566197
KITopen ID: 1000056619
Verlag Karlsruhe
Umfang XIII, 167 S.
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdaten 16.06.2016
Referent/Betreuer Prof. R. Stiefelhagen
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