Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118499
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Type: Conference paper
Title: Visual Question Answering with memory-augmented network
Author: Ma, C.
Shen, C.
Dick, A.
Wu, Q.
Wang, P.
Van Den Hengel, A.
Reid, I.
Citation: Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR 20418, 2018 / pp.6975-6984
Publisher: IEEE
Issue Date: 2018
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781538664209
ISSN: 1063-6919
Conference Name: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18 Jun 2018 - 22 Jun 2018 : Salt Lake City, Utah)
Statement of
Responsibility: 
Chao Ma, Chunhua Shen, Anthony Dick, Qi Wu, Peng Wang, Anton van den Hengel, and Ian Reid
Abstract: In this paper, we exploit memory-augmented neural networks to predict accurate answers to visual questions, even when those answers rarely occur in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results in two large-scale benchmark datasets show the favorable performance of the proposed algorithm with the comparison to state of the art.
Rights: Copyright © 2018 by The Institute of Electrical and Electronics Engineers, Inc.
RMID: 0030108840
DOI: 10.1109/CVPR.2018.00729
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FL130100102
Appears in Collections:Computer Science publications

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