Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120112
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Type: Conference paper
Title: Vision-and-language navigation: interpreting visually-grounded navigation instructions in real environments
Author: Anderson, P.
Wu, Q.
Teney, D.
Bruce, J.
Johnson, M.
Sünderhauf, N.
Reid, I.
Gould, S.
Hengel, A.
Citation: Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)), 2018 / vol.abs/1711.07280, pp.3674-3683
Publisher: IEEE
Issue Date: 2018
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781538664209
ISSN: 2575-7075
Conference Name: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18 Jun 2018 - 23 Jun 2018 : Salt Lake City, UT)
Statement of
Responsibility: 
Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko S, underhauf, Ian Reid, Stephen Gould, Anton van den Hengel
Abstract: A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1.
Rights: © 2018 IEEE
RMID: 0030084428
DOI: 10.1109/CVPR.2018.00387
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/DP160102156
Appears in Collections:Computer Science publications

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