Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/116322
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Type: Conference paper
Title: Explicit knowledge-based reasoning for visual question answering
Author: Wang, P.
Wu, Q.
Shen, C.
Dick, A.
Van Den Hengel, A.
Citation: Proceedings of the twenty-sixth International Joint Conference on Artificial Intelligence, 2017 / Sierra, C. (ed./s), pp.1290-1296
Publisher: IJCAI
Publisher Place: online
Issue Date: 2017
ISBN: 9780999241103
ISSN: 1045-0823
Conference Name: 26th International Joint Conference on Artificial Intelligence (IJCAI-17) (19 Aug 2017 - 26 Aug 2017 : Melbourne)
Statement of
Responsibility: 
Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, Anton van den Hengel
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.
Rights: Copyright © 2017 International Joint Conferences on Artificial Intelligence
RMID: 0030076998
DOI: 10.24963/ijcai.2017/179
Published version: https://www.ijcai.org/proceedings/2017/179
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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