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Unsupervised learning of object landmarks through conditional image generation

Abstract:

We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision. We cast this as the problem of generating images that combine the appearance of the object as seen in a first example image with the geometry of the object as seen in a second example image, where the two examples differ by a viewpoint change and/or an object deformation. In order to factorize appearance and geometry, we introduce a tight bottleneck ...

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Publication status:
In press
Peer review status:
Peer reviewed
Version:
Publisher's Version

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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
Clarendon Fund More from this funder
Publisher:
Curran Associates Publisher's website
Volume:
31
Pages:
4016-4027
Publication date:
2018-12-31
Acceptance date:
2018-09-05
Pubs id:
pubs:943529
URN:
uri:f562c769-774d-4d49-821d-2189bb213d00
UUID:
uuid:f562c769-774d-4d49-821d-2189bb213d00
Local pid:
pubs:943529

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