Deep Learning on Lie Groups for Skeleton-based Action Recognition
dc.contributor.author
Huang, Zhiwu
dc.contributor.author
Wan, Chengde
dc.contributor.author
Probst, Thomas
dc.contributor.author
Van Gool, Luc
dc.date.accessioned
2018-11-21T13:12:38Z
dc.date.available
2018-11-21T13:12:38Z
dc.date.issued
2017
dc.identifier.isbn
978-1-5386-0457-1
en_US
dc.identifier.isbn
978-1-5386-0458-8
en_US
dc.identifier.other
10.1109/CVPR.2017.137
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/305323
dc.identifier.doi
10.3929/ethz-b-000184741
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Lie groups
en_US
dc.subject
Feature extraction
en_US
dc.subject
Image classification
en_US
dc.subject
Image motion analysis
en_US
dc.subject
Image recognition
en_US
dc.subject
Image representation
en_US
dc.subject
Learning (artificial intelligence)
en_US
dc.subject
3D classification
en_US
dc.subject
Lie group structure
en_US
dc.subject
Appropriate Lie group
en_US
dc.subject
Deep learning methods
en_US
dc.subject
Deep network architecture
en_US
dc.subject
High feature dimensionality
en_US
dc.subject
Input Lie group
en_US
dc.subject
Logarithm mapping layer
en_US
dc.subject
Skeleton
en_US
dc.subject
Standard 3D human action recognition datasets
en_US
dc.subject
Computer architecture
en_US
dc.subject
Machine learning
en_US
dc.subject
Manifolds
en_US
dc.subject
Neural networks
en_US
dc.subject
Three-dimensional displays
en_US
dc.subject
Transforms
en_US
dc.title
Deep Learning on Lie Groups for Skeleton-based Action Recognition
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2017-11-09
ethz.book.title
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
en_US
ethz.pages.start
1243
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ethz.pages.end
1252
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ethz.version.deposit
acceptedVersion
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ethz.event
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
en_US
ethz.event.location
Honolulu, HI, USA
en_US
ethz.event.date
July 21-26, 2017
en_US
ethz.grant
Remote Medical Diagnostitian
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ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
ethz.grant.agreementno
610902
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
FP7
ethz.date.deposited
2017-09-13T22:08:24Z
ethz.source
FORM
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
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ethz.rosetta.installDate
2018-11-21T13:12:53Z
ethz.rosetta.lastUpdated
2021-02-15T02:35:55Z
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true
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true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/184741
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/227362
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