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Modelling and unsupervised learning of symmetric deformable object categories

Abstract:

We propose a new approach to model and learn, without manual supervision, the symmetries of natural objects, such as faces or flowers, given only images as input. It is well known that objects that have a symmetric structure do not usually result in symmetric images due to articulation and perspective effects. This is often tackled by seeking the intrinsic symmetries of the underlying 3D shape, which is very difficult to do when the latter cannot be recovered reliably from data. We show that,...

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Publication status:
Published
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
Oxford college:
New College
Role:
Author
Publisher:
Neural Information Processing Systems Publisher's website
Acceptance date:
2018-09-05
Pubs id:
pubs:950984
URN:
uri:2fc95c7c-21e9-4b80-849f-34c11b372067
UUID:
uuid:2fc95c7c-21e9-4b80-849f-34c11b372067
Local pid:
pubs:950984

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