Visual Question Answering: A Deep Interactive Framework for Post-Disaster Management and Damage Assessment

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dc.contributor.authorSarkar, Argho
dc.contributor.authorRahnemoonfar, Maryam
dc.date.accessioned2021-08-16T20:05:32Z
dc.date.available2021-08-16T20:05:32Z
dc.date.issued2021-07-10
dc.descriptionClimate Change AIen
dc.description.urihttps://www.climatechange.ai/papers/icml2021/52/slides.pdfen
dc.format.extent13 pagesen
dc.genreconference papers and proceedingsen
dc.genrepresentations (communicate events)en
dc.identifierdoi:10.13016/m2ylmc-gg8t
dc.identifier.citationSarkar, Argho; Rahnemoonfar, Maryam; Visual Question Answering: A Deep Interactive Framework for Post-Disaster Management and Damage Assessment; Climate Change AI, 10 July, 2021; https://www.climatechange.ai/papers/icml2021/52/slides.pdfen
dc.identifier.urihttp://hdl.handle.net/11603/22470
dc.language.isoenen
dc.publisherClimate Change AIen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en
dc.titleVisual Question Answering: A Deep Interactive Framework for Post-Disaster Management and Damage Assessmenten
dc.typeTexten
dcterms.creatorhttps://orcid.org/0000-0002-8545-0830en

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