Explicit knowledge-based reasoning for visual question answering

dc.contributor.authorWang, P.
dc.contributor.authorWu, Q.
dc.contributor.authorShen, C.
dc.contributor.authorDick, A.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.conference26th International Joint Conference on Artificial Intelligence (IJCAI-17) (19 Aug 2017 - 26 Aug 2017 : Melbourne)
dc.contributor.editorSierra, C.
dc.date.issued2017
dc.description.abstractWe describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a largescale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can explain the reasoning by which it developed its answer. It is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in testing. We also provide a dataset and a protocol by which to evaluate general visual question answering methods.
dc.description.statementofresponsibilityPeng Wang, Qi Wu, Chunhua Shen, Anthony Dick, Anton van den Hengel
dc.identifier.citationIJCAI : proceedings of the conference / sponsored by the International Joint Conferences on Artificial Intelligence, 2017 / Sierra, C. (ed./s), vol.0, pp.1290-1296
dc.identifier.doi10.24963/ijcai.2017/179
dc.identifier.isbn9780999241103
dc.identifier.issn1045-0823
dc.identifier.orcidWu, Q. [0000-0003-3631-256X]
dc.identifier.orcidDick, A. [0000-0001-9049-7345]
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]
dc.identifier.urihttp://hdl.handle.net/2440/116322
dc.language.isoen
dc.publisherIJCAI
dc.publisher.placeonline
dc.rightsCopyright © 2017 International Joint Conferences on Artificial Intelligence
dc.source.urihttps://www.ijcai.org/proceedings/2017/179
dc.titleExplicit knowledge-based reasoning for visual question answering
dc.typeConference paper
pubs.publication-statusPublished

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