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dc.contributor.authorJoshi, Ajjenen_US
dc.contributor.authorGhosh, Soumyaen_US
dc.contributor.authorBetke, Margriten_US
dc.contributor.authorSclaroff, Stanen_US
dc.contributor.authorPfister, Hanspeteren_US
dc.coverage.spatialHonolulu, HIen_US
dc.date.accessioned2018-02-05T18:24:40Z
dc.date.available2018-02-05T18:24:40Z
dc.date.issued2017-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000418371400049&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationAjjen Joshi, Soumya Ghosh, Margrit Betke, Stan Sclaroff, Hanspeter Pfister. 2017. "Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks." 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017). 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, 2016-07-21 - 2016-07-26
dc.identifier.issn1063-6919
dc.identifier.urihttps://hdl.handle.net/2144/26687
dc.description.abstractBuilding robust classifiers trained on data susceptible to group or subject-specific variations is a challenging pattern recognition problem. We develop hierarchical Bayesian neural networks to capture subject-specific variations and share statistical strength across subjects. Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy. We also develop methods for adapting our model to new subjects when a small number of subject-specific personalization data is available. Finally, we investigate active learning algorithms for interactively labeling personalization data in resource-constrained scenarios. Focusing on the problem of gesture recognition where inter-subject variations are commonplace, we demonstrate the effectiveness of our proposed techniques. We test our framework on three widely used gesture recognition datasets, achieving personalization performance competitive with the state-of-the-art.en_US
dc.description.urihttp://openaccess.thecvf.com/content_cvpr_2017/html/Joshi_Personalizing_Gesture_Recognition_CVPR_2017_paper.html
dc.description.urihttp://openaccess.thecvf.com/content_cvpr_2017/html/Joshi_Personalizing_Gesture_Recognition_CVPR_2017_paper.html
dc.description.urihttp://openaccess.thecvf.com/content_cvpr_2017/html/Joshi_Personalizing_Gesture_Recognition_CVPR_2017_paper.html
dc.format.extent455 - 464 (10)en_US
dc.languageEnglish
dc.publisherIEEEen_US
dc.relation.ispartof30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
dc.rightsThis CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark on the paper, it is identical to the version available on IEEE Xplore.en_US
dc.subjectScience & technologyen_US
dc.subjectComputer science, artificial intelligenceen_US
dc.subjectComputer science, theory & methodsen_US
dc.subjectEngineering, electrical & electronicen_US
dc.subjectComputer scienceen_US
dc.subjectEngineeringen_US
dc.subjectAdaptationen_US
dc.subjectBayesian neural network (BNN)en_US
dc.titlePersonalizing gesture recognition using hierarchical bayesian neural networksen_US
dc.typeConference materialsen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1109/CVPR.2017.56
pubs.elements-sourceweb-of-scienceen_US
pubs.notesWaiveren_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-0711-4313 (Sclaroff, Stan)


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