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Learning to adapt for stereo

Abstract:

Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are successful, they often fail to generalize to unseen variations in the environment, making them less suitable for practical applications such as autonomous driving. In this work, we introduce a ``learning-to-adapt'' framework that enables deep stereo methods to continuously adapt to new target domains in an unsupervised m...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/CVPR.2019.00989

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Name:
Leverhulme Trust
Grant:
RPG-2012-544
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Name:
European Commission
Grant:
321162
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/N019474/1
Publisher:
IEEE
Host title:
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Journal:
Computer Vision and Pattern Recognition More from this journal
Pages:
9653-9662
Publication date:
2020-01-09
Acceptance date:
2019-03-02
DOI:
ISSN:
2575-7075
ISBN:
9781728132938
Keywords:
Pubs id:
pubs:996409
UUID:
uuid:fd4a46cc-b6d6-44ae-b6b1-d1873eb183ba
Local pid:
pubs:996409
Source identifiers:
996409
Deposit date:
2019-05-07

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