A Multi-Task Bayesian Deep Neural Net for Detecting Life-Threatening Infant Incidents From Head Images

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorWang, Tzu-Jui Juliusen_US
dc.contributor.authorLaaksonen, Jormaen_US
dc.contributor.authorLiao, Yi-Pingen_US
dc.contributor.authorWu, Bo-Zongen_US
dc.contributor.authorShen, Shih-Yunen_US
dc.contributor.departmentProfessorship Kaski Samuelen_US
dc.contributor.departmentCentre of Excellence in Computational Inference, COINen_US
dc.contributor.departmentYun Yun AI Baby Camera Co., Ltd.en_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.date.accessioned2020-01-02T14:12:08Z
dc.date.available2020-01-02T14:12:08Z
dc.date.issued2019en_US
dc.description.abstractThe notorious incident of sudden infant death syndrome (SIDS) can easily happen to a newborn due to many environmental factors. To prevent such tragic incidents from happening, we propose a multi-task deep learning framework that detects different facial traits and two life-threatening indicators, i.e. which facial parts are occluded or covered, by analyzing the infant head image. Furthermore, we extend and adapt the recently developed models that capture data-dependent uncertainty from noisy observations for our application. The experimental results show significant improvements on YunInfants dataset across most of the tasks over the models that simply adopt the regular cross-entropy losses without addressing the effect of the underlying uncertainties.en
dc.description.versionPeer revieweden
dc.format.extent5
dc.format.extent3006-3010
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationWang , T-J J , Laaksonen , J , Liao , Y-P , Wu , B-Z & Shen , S-Y 2019 , A Multi-Task Bayesian Deep Neural Net for Detecting Life-Threatening Infant Incidents From Head Images . in 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings . , 8803332 , IEEE , pp. 3006-3010 , IEEE International Conference on Image Processing , Taipei , Taiwan, Republic of China , 22/09/2019 . https://doi.org/10.1109/ICIP.2019.8803332en
dc.identifier.doi10.1109/ICIP.2019.8803332en_US
dc.identifier.isbn978-1-5386-6249-6
dc.identifier.otherPURE UUID: ea546b89-ff4c-4a94-9632-19e89b10ffbeen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/ea546b89-ff4c-4a94-9632-19e89b10ffbeen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/38942535/SCI_Wang_Laaksonen_A_Multi_task_Bayesian_icip19_camera.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/42275
dc.identifier.urnURN:NBN:fi:aalto-202001021386
dc.language.isoenen
dc.relation.ispartofIEEE International Conference on Image Processingen
dc.relation.ispartofseries2019 IEEE International Conference on Image Processing (ICIP)en
dc.rightsopenAccessen
dc.titleA Multi-Task Bayesian Deep Neural Net for Detecting Life-Threatening Infant Incidents From Head Imagesen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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