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https://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.D. Gould, S. Hengel, A.V.D. |
Citation: | Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 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 |
DOI: | 10.1109/CVPR.2018.00387 |
Grant ID: | http://purl.org/au-research/grants/arc/CE140100016 http://purl.org/au-research/grants/arc/DP160102156 |
Published version: | http://dx.doi.org/10.1109/cvpr.2018.00387 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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