Human‐centered attention‐aware networks for action recognition

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 10968-10987 RESEARCH ARTICLE Human-centered attention-aware networks for action recognition Shuai Liu, Shuai Liu orcid.org/0000-0001-9909-0664 School of Educational Science, Hunan Normal University, Changsha, China Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha, China College of Information Science and Engineering, Hunan Normal University, Changsha, ChinaSearch for more papers by this authorYating Li, Yating Li College of Information Science and Engineering, Hunan Normal University, Changsha, ChinaSearch for more papers by this authorWeina Fu, Corresponding Author Weina Fu fuwn@hunnu.edu.cn College of Information Science and Engineering, Hunan Normal University, Changsha, China Correspondence Weina Fu, College of Information Science and Engineering, Hunan Normal University, Changsha, China. Email: fuwn@hunnu.edu.cnSearch for more papers by this author Shuai Liu, Shuai Liu orcid.org/0000-0001-9909-0664 School of Educational Science, Hunan Normal University, Changsha, China Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University, Changsha, China College of Information Science and Engineering, Hunan Normal University, Changsha, ChinaSearch for more papers by this authorYating Li, Yating Li College of Information Science and Engineering, Hunan Normal University, Changsha, ChinaSearch for more papers by this authorWeina Fu, Corresponding Author Weina Fu fuwn@hunnu.edu.cn College of Information Science and Engineering, Hunan Normal University, Changsha, China Correspondence Weina Fu, College of Information Science and Engineering, Hunan Normal University, Changsha, China. Email: fuwn@hunnu.edu.cnSearch for more papers by this author First published: 23 August 2022 https://doi.org/10.1002/int.23029Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Action recognition in video is a research hot spot in the field of computer vision. Learning important clues in video context has significant effect to promote the interaction prediction and gesture recognition. Most existing methods infer the interactions between actor and context through relational reasoning methods. While these relational features contribute to improve the salience of action performance, the error will occur when the salient region is irrelevant to the recognized action. Therefore, this paper establishes a human-centered attention mechanism that dynamically highlights regions associated with action recognition according to target appearance to selectively recognize the human-object interaction action. The effectiveness of the proposed mechanism is verified on the AVA2.2 data set, and the visualized attention map further shows that the proposed attention mechanism can effectively recognize human-centered strongly correlated action. Open Research DATA AVAILABILITY STATEMENT Data sharing is not applicable to this article as no new data were created or analyzed in this study. Citing Literature Volume37, Issue12December 2022Pages 10968-10987 RelatedInformation