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Cross-Agent Action Recognition
Wang, Hongsong; Wang, Liang
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215
2018-10-01
Volume28Issue:10Pages:2908-2919
Corresponding AuthorWang, Liang(wangliang@nlpr.ia.ac.cn)
AbstractAn action is something which is done by an agent. Most action recognition researchers merely focus on the actions to be recognized, and ignore the differences of agents. Philosophers and behaviorists discover that actions are common among many species, but are performed in different ways and with different levels of sophistication. In this paper, in order to bridge action recognition tasks between different agents, we introduce a new problem, cross-agent action recognition, i.e., recognizing action for one particular agent (target) while training from other agents (source). We model this problem under three different scenarios: single source and single target, multiple sources and single target, and multiple sources and multiple targets. To this end, corresponding methods based on transfer learning are proposed to address these problems. We further design three different strategies to model the situation when a partial labeled data is provided for the target. Experimental results show that the performances of the transfer method are generally better than those of the comparative method without transfer learning, especially when we have multiple sources. Particularly, the transfer method outperforms the others significantly when the source is a human adult. In addition, cross-agent method significantly improves the results when partially labeled data is provided for the target. These demonstrate that for action recognition, knowledge can be transferred across different agents. A straightforward application of this finding is to use human action (training data is abundant) data to enhance animal action recognition.
KeywordCross-agent human action recognition animal action recognition
DOI10.1109/TCSVT.2017.2746092
WOS KeywordVIEW ACTION RECOGNITION
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61420106015] ; Beijing Natural Science Foundation[4162058]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000448517900040
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22804
Collection智能感知与计算研究中心
Corresponding AuthorWang, Liang
AffiliationUniv Chinese Acad Sci, Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit,Inst Automat, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Hongsong,Wang, Liang. Cross-Agent Action Recognition[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2018,28(10):2908-2919.
APA Wang, Hongsong,&Wang, Liang.(2018).Cross-Agent Action Recognition.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,28(10),2908-2919.
MLA Wang, Hongsong,et al."Cross-Agent Action Recognition".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28.10(2018):2908-2919.
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