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Unsupervised learning of landmarks by descriptor vector exchange

Abstract:

Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision. However, this method does not explicitly guarantee that the learned landmarks are consistent with changes between different instances of the same object, such as different facial identities. In this paper, we develop a new perspective on the equivariance approach by noting that dense landmark detectors can be interpret...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICCV.2019.00646
Publication website:
https://ieeexplore.ieee.org/xpl/conhome/8972782/proceeding

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
New College
Role:
Author
Publisher:
IEEE Publisher's website
Pages:
6360-6370
Host title:
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publication date:
2020-02-27
Acceptance date:
2019-07-22
Event location:
Seoul, South Korea
Event website:
http://iccv2019.thecvf.com/
Event start date:
2019-10-27T00:00:00Z
Event end date:
2019-11-02T00:00:00Z
DOI:
EISBN:
978-1-7281-4803-8
EISSN:
2380-7504
Source identifiers:
1061100
Language:
English
Keywords:
Pubs id:
pubs:1061100
UUID:
uuid:7b0bd6f8-e7fb-4c84-86ec-35e5b9e762e4
Local pid:
pubs:1061100
Deposit date:
2019-10-07

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