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Self-supervised learning of geometrically stable features through probabilistic introspection

Abstract:

Self-supervision can dramatically cut back the amount of manually-labeled data required to train deep neural networks. While self-supervision has usually been considered for tasks such as image classification, in this paper we aim at extending it to geometry-oriented tasks such as semantic matching and part detection. We do so by building on several recent ideas in unsupervised landmark detection. Our approach learns dense distinctive visual descriptors from an unlabeled dataset of images usi...

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Publication status:
In press
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
New College
Role:
Author
Publisher:
Institute for Electrical and Electronics Engineers Publisher's website
Publication date:
2018-01-01
Acceptance date:
2018-06-14
Pubs id:
pubs:943534
URN:
uri:b488e633-ede6-4d39-a524-6f07e76914fc
UUID:
uuid:b488e633-ede6-4d39-a524-6f07e76914fc
Local pid:
pubs:943534

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