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dc.contributor.authorWang, P.-
dc.contributor.authorWu, Q.-
dc.contributor.authorShen, C.-
dc.contributor.authorDick, A.-
dc.contributor.authorVan Den Hengel, A.-
dc.contributor.editorSierra, C.-
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.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.rightsCopyright © 2017 International Joint Conferences on Artificial Intelligence-
dc.titleExplicit knowledge-based reasoning for visual question answering-
dc.typeConference paper-
dc.contributor.conference26th International Joint Conference on Artificial Intelligence (IJCAI-17) (19 Aug 2017 - 26 Aug 2017 : Melbourne)-
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]-
Appears in Collections:Aurora harvest 3
Australian Institute for Machine Learning publications
Computer Science publications

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