Explicit knowledge-based reasoning for visual question answering
Date
2017
Authors
Wang, P.
Wu, Q.
Shen, C.
Dick, A.
Van Den Hengel, A.
Editors
Sierra, C.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
IJCAI : proceedings of the conference / sponsored by the International Joint Conferences on Artificial Intelligence, 2017 / Sierra, C. (ed./s), vol.0, pp.1290-1296
Statement of Responsibility
Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, Anton van den Hengel
Conference Name
26th International Joint Conference on Artificial Intelligence (IJCAI-17) (19 Aug 2017 - 26 Aug 2017 : Melbourne)
Abstract
We 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.
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Dissertation Note
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Copyright © 2017 International Joint Conferences on Artificial Intelligence