Harnessing lab knowledge for real-world action recognition

Ma, Zhigang, Yang, Yi, Nie, Feiping, Sebe, Nicu, Yan, Shuicheng and Hauptmann, Alexander G. (2014) Harnessing lab knowledge for real-world action recognition. International Journal of Computer Vision, 109 1-2: 60-73. doi:10.1007/s11263-014-0717-5

Author Ma, Zhigang
Yang, Yi
Nie, Feiping
Sebe, Nicu
Yan, Shuicheng
Hauptmann, Alexander G.
Title Harnessing lab knowledge for real-world action recognition
Journal name International Journal of Computer Vision   Check publisher's open access policy
ISSN 1573-1405
Publication date 2014-08-01
Year available 2014
Sub-type Article (original research)
DOI 10.1007/s11263-014-0717-5
Open Access Status Not yet assessed
Volume 109
Issue 1-2
Start page 60
End page 73
Total pages 14
Place of publication New York, NY, United States
Publisher Springer New York
Language eng
Formatted abstract
Much research on human action recognition has been oriented toward the performance gain on lab-collected datasets. Yet real-world videos are more diverse, with more complicated actions and often only a few of them are precisely labeled. Thus, recognizing actions from these videos is a tough mission. The paucity of labeled real-world videos motivates us to "borrow" strength from other resources. Specifically, considering that many lab datasets are available, we propose to harness lab datasets to facilitate the action recognition in real-world videos given that the lab and real-world datasets are related. As their action categories are usually inconsistent, we design a multi-task learning framework to jointly optimize the classifiers for both sides. The general Schatten p -norm is exerted on the two classifiers to explore the shared knowledge between them. In this way, our framework is able to mine the shared knowledge between two datasets even if the two have different action categories, which is a major virtue of our method. The shared knowledge is further used to improve the action recognition in the real-world videos. Extensive experiments are performed on real-world datasets with promising results.
Keyword Action recognition
Lab to real-world
Transfer learning
General Schatten-p norm
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 13 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 13 times in Scopus Article | Citations
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