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Transferring dense pose to proximal animal classes

Abstract:

Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail. In principle, the same approach could be extended to any animal class, but the effort required for collecting new annotations for each case makes this strategy impractical, despite important applications in natural conservation, science and business. We show that, at least for proximal animal classes such as chimpanzees, it is po...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/cvpr42600.2020.00528

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Department:
ENGINEERING SCIENCE
Sub department:
Engineering Science
Role:
Author
Publisher:
IEEE
Host title:
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Pages:
5232-5241
Publication date:
2020-08-05
Acceptance date:
2020-02-23
Event title:
CVPR 2020
Event location:
Virtual event
Event website:
http://cvpr2020.thecvf.com/
Event start date:
2020-06-14
Event end date:
2020-06-19
DOI:
EISSN:
2575-7075
EISBN:
978-1-7281-7168-5
Language:
English
Keywords:
Pubs id:
1126063
Local pid:
pubs:1126063
Deposit date:
2020-08-14

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