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Dual-Stream Multiple Instance Learning for Depression Detection With Facial Expression Videos
16 references·1 citing·51 second hopsampled
Dual-Stream Multiple Instance Learning for Depression Detection With Facial Expression Videos (2022)Dual-Stream Multiple Instance Learning fo…MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection (2021)MDN: A Deep Maximization-Diff…Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic (2020)Prevalence of Depression Symp…Automatic depression recognition using CNN with attention mechanism from videos (2020)Automatic depression recognit…A Deep Multiscale Spatiotemporal Network for Assessing Depression From Facial Dynamics (2020)A Deep Multiscale Spatiotempo…Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features (2018)Video-Based Depression Level …Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys (2017)Socio-economic variations in …Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics (2017)Automated Depression Diagnosi…AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017)AffectNet: A Database for Fac…Recurrent Neural Networks for Emotion Recognition in Video (2015)Recurrent Neural Networks for…Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding (2015)Automated Depression Diagnosi…AVEC 2014 (2014)AVEC 2014Automatic facial expression recognition using features of salient facial patches (2014)Automatic facial expression r…Diagnosis of depression by behavioural signals (2013)Diagnosis of depression by be…Depression recognition based on dynamic facial and vocal expression features using partial least square regression (2013)Depression recognition based …AVEC 2013 (2013)AVEC 2013Nonverbal communication in depression (1989)Nonverbal communication in de…DEP-Former: Multimodal Depression Recognition Based on Facial Expressions and Audio Features via Emotional Changes (2024)DEP-Former: Multimodal Depres…Development of a Rating Scale for Primary Depressive Illness (1967)Comparison of Beck Depression Inventories-IA and-II in Psychiatric Outpatients (1996)Affective Computing (1997)AVEC 2016 (2016)EEG Based Emotion Recognition: A Tutorial and Review (2022)Facial Action Coding System (1978)Speech emotion recognition using hidden Markov models (2003)Survey on speech emotion recognition: Features, classification schemes, and databases (2010)Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors (2016)Artificial Intelligent System for Automatic Depression Level Analysis Through Visual and Vocal Expressions (2017)Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding (2017)Facial expression recognition from video sequences: temporal and static modeling (2003)Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior (2005)Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010)Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2020)Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild (2021)Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations (2023)OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit (2009)Collecting Large, Richly Annotated Facial-Expression Databases from Movies (2012)EmoNets: Multimodal deep learning approaches for emotion recognition in video (2015)Hierarchical committee of deep convolutional neural networks for robust facial expression recognition (2016)Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition (2017)Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives (2020)The Epidemiology of Major Depressive Disorder (2003)Automatic Assessment of Depression Based on Visual Cues: A Systematic Review (2017)Automated depression analysis using convolutional neural networks from speech (2018)Deep learning for depression recognition with audiovisual cues: A review (2021)A Coefficient of Agreement for Nominal Scales (1960)Intraclass correlations: Uses in assessing rater reliability. 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References

  • 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild2,335 cited
  • 2020Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic2,228 cited
  • 2017Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys779 cited
  • 2014Automatic facial expression recognition using features of salient facial patches601 cited
  • 2013AVEC 2013584 cited
  • 2014AVEC 2014475 cited
  • 2015Recurrent Neural Networks for Emotion Recognition in Video386 cited
  • 2017Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics260 cited
  • 2013Depression recognition based on dynamic facial and vocal expression features using partial least square regression224 cited
  • 2020Automatic depression recognition using CNN with attention mechanism from videos179 cited
  • 2018Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features161 cited
  • 2015Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding141 cited
  • 1989Nonverbal communication in depression141 cited
  • 2013Diagnosis of depression by behavioural signals139 cited

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  • 2021MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection137 cited
  • 2020A Deep Multiscale Spatiotemporal Network for Assessing Depression From Facial Dynamics116 cited
  • Cited by

    • 2024DEP-Former: Multimodal Depression Recognition Based on Facial Expressions and Audio Features via Emotional Changes31 cited