Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111388
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Type: Conference paper
Title: Graph-structured representations for visual question answering
Author: Teney, D.
Liu, L.
van den Hengel, A.
Citation: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017 / vol.2017-January, pp.3233-3241
Publisher: IEEE
Publisher Place: Online
Issue Date: 2017
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781538604588
ISSN: 1063-6919
Conference Name: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (21 Jul 2017 - 26 Jul 2017 : Honolulu, HI)
Statement of
Responsibility: 
Damien Teney, Lingqiao Liu, Anton van den Hengel
Abstract: This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions. A key challenge in VQA is to require joint reasoning over the visual and text domains. The predominant CNN/LSTM-based approach to VQA is limited by monolithic vector representations that largely ignore structure in the scene and in the question. CNN feature vectors cannot effectively capture situations as simple as multiple object instances, and LSTMs process questions as series of words, which do not reflect the true complexity of language structure. We instead propose to build graphs over the scene objects and over the question words, and we describe a deep neural network that exploits the structure in these representations. We show that this approach achieves significant improvements over the state-of-the-art, increasing accuracy from 71.2% to 74.4% in accuracy on the abstract scenes multiple-choice benchmark, and from 34.7% to 39.1% in accuracy over pairs of balanced scenes, i.e. images with fine-grained differences and opposite yes/no answers to a same question.
Rights: © 2017 IEEE
RMID: 0030080414
DOI: 10.1109/CVPR.2017.344
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8097368
Appears in Collections:Computer Science publications

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