L773:9781665487399 OR L773:9781665487405
Search: L773:9781665487399 OR L773:9781665487405 > Bootstrapped Repres...
- 4 of 5
- Previous record
- Next record
- To hitlist
Bootstrapped Representation Learning for Skeleton-Based Action Recognition
-
- Moliner, Olivier (author)
- Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Sony Europe B.V.
-
- Huang, Sangxia (author)
- Sony Europe B.V.
-
- Astrom, Kalle (author)
- Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH,Sony Europe B.V.
- (creator_code:org_t)
- 2022
- 2022
- English 11 s.
- In: Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. - 2160-7508 .- 2160-7516. - 9781665487399 ; 2022-June, s. 4153-4163
- Conference paper (peer-reviewed)
Abstract
Subject headings
Close
- In this work, we study self-supervised representation learning for 3D skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL) for representation learning on skeleton sequence data and propose a new data augmentation strategy including two asymmetric transformation pipelines. We also introduce a multi-viewpoint sampling method that leverages multiple viewing angles of the same action captured by different cameras. In the semi-supervised setting, we show that the performance can be further improved by knowledge distillation from wider networks, leveraging once more the unlabeled samples. We conduct extensive experiments on the NTU-60, NTU-120 and PKU-MMD datasets to demonstrate the performance of our proposed method. Our method consistently outperforms the current state of the art on linear evaluation, semi-supervised and transfer learning benchmarks.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publication and Content Type
- kon (subject category)
- ref (subject category)
Find in a library
- Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognitio... (Search for host publication in LIBRIS)
To the university's database
- 4 of 5
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Moliner, Olivier
- Huang, Sangxia
- Astrom, Kalle
- About the subject
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Computer and Inf ...
- and Computer Vision ...
- Articles in the publication
- Proceedings - 20 ...
- By the university
- Lund University
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
