Self-trained deep ordinal regression for end-to-end video anomaly detection

Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative...

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Main Authors: PANG, Guansong, YAN, Cheng, SHEN, Chunhua, HENGEL, Anton Van Den, BAI, Xiao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/7022
https://ink.library.smu.edu.sg/context/sis_research/article/8025/viewcontent/2012.02950.pdf
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spelling sg-smu-ink.sis_research-80252022-03-17T15:04:08Z Self-trained deep ordinal regression for end-to-end video anomaly detection PANG, Guansong YAN, Cheng SHEN, Chunhua HENGEL, Anton Van Den BAI, Xiao Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model’s generalizability on small depression samples. Further, unlike existing studies that focus on learning depression signs from static data without considering temporal dynamics, we focus on longitudinal data because i) temporal changes in personal development and family environment can provide critical cues for psychiatric disorders and ii) it may enable us to predict depression before the illness actually occurs. Extensive experimental results on child depression data show that our model is able to i) achieve nearly perfect performance in depression detection and ii) accurately predict depression 2-4 years before the clinical diagnosis, substantially outperforming seven competing methods. 2020-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7022 https://ink.library.smu.edu.sg/context/sis_research/article/8025/viewcontent/2012.02950.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
spellingShingle Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
PANG, Guansong
YAN, Cheng
SHEN, Chunhua
HENGEL, Anton Van Den
BAI, Xiao
Self-trained deep ordinal regression for end-to-end video anomaly detection
description Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model’s generalizability on small depression samples. Further, unlike existing studies that focus on learning depression signs from static data without considering temporal dynamics, we focus on longitudinal data because i) temporal changes in personal development and family environment can provide critical cues for psychiatric disorders and ii) it may enable us to predict depression before the illness actually occurs. Extensive experimental results on child depression data show that our model is able to i) achieve nearly perfect performance in depression detection and ii) accurately predict depression 2-4 years before the clinical diagnosis, substantially outperforming seven competing methods.
format text
author PANG, Guansong
YAN, Cheng
SHEN, Chunhua
HENGEL, Anton Van Den
BAI, Xiao
author_facet PANG, Guansong
YAN, Cheng
SHEN, Chunhua
HENGEL, Anton Van Den
BAI, Xiao
author_sort PANG, Guansong
title Self-trained deep ordinal regression for end-to-end video anomaly detection
title_short Self-trained deep ordinal regression for end-to-end video anomaly detection
title_full Self-trained deep ordinal regression for end-to-end video anomaly detection
title_fullStr Self-trained deep ordinal regression for end-to-end video anomaly detection
title_full_unstemmed Self-trained deep ordinal regression for end-to-end video anomaly detection
title_sort self-trained deep ordinal regression for end-to-end video anomaly detection
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/7022
https://ink.library.smu.edu.sg/context/sis_research/article/8025/viewcontent/2012.02950.pdf
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