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dc.contributor.authorHuang, Jia-Hong
dc.contributor.authorAlfadly, Modar
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2017-12-28T07:32:16Z
dc.date.available2017-12-28T07:32:16Z
dc.date.issued2017-03-19
dc.identifier.urihttp://hdl.handle.net/10754/626564
dc.description.abstractTaking an image and question as the input of our method, it can output the text-based answer of the query question about the given image, so called Visual Question Answering (VQA). There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question. We formulate the basic questions generation problem as a LASSO optimization problem, and also propose a criterion about how to exploit these basic questions to help answer main question. Our method is evaluated on the challenging VQA dataset and yields state-of-the-art accuracy, 60.34% in open-ended task.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1703.06492v2
dc.relation.urlhttp://arxiv.org/pdf/1703.06492v2
dc.rightsArchived with thanks to arXiv
dc.titleVQABQ: Visual Question Answering by Basic Questions
dc.typePreprint
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.eprint.versionPre-print
dc.identifier.arxivid1703.06492
kaust.personHuang, Jia-Hong
kaust.personAlfadly, Modar
kaust.personGhanem, Bernard
refterms.dateFOA2018-06-13T11:15:06Z


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