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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Yun Yun AI Baby Camera Co., Ltd.

Abstract

The 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.

Details

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing - Taipei, Taiwan, Republic of China
Duration: 22 Sep 201925 Sep 2019
Conference number: 26

Conference

ConferenceIEEE International Conference on Image Processing
Abbreviated titleICIP
CountryTaiwan, Republic of China
CityTaipei
Period22/09/201925/09/2019

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