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Automatic Depression Recognition With an Ensemble of Multimodal Spatio-Temporal Routing Features
16 references·0 citing·49 second hop
Automatic Depression Recognition With an Ensemble of Multimodal Spatio-Temporal Routing Features (2025)Automatic Depression Recognition With an …Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals (2023)Transformer-based multimodal …Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022)Time for united action on dep…NeuroKit2: A Python toolbox for neurophysiological signal processing (2021)NeuroKit2: A Python toolbox f…MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection (2021)MDN: A Deep Maximization-Diff…Spectral Representation of Behaviour Primitives for Depression Analysis (2020)Spectral Representation of Be…Multimodal Spatiotemporal Representation for Automatic Depression Level Detection (2020)Multimodal Spatiotemporal Rep…Depression Level Prediction Using Deep Spatiotemporal Features and Multilayer Bi-LTSM (2020)Depression Level Prediction U…A Deep Multiscale Spatiotemporal Network for Assessing Depression From Facial Dynamics (2020)A Deep Multiscale Spatiotempo…AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019)AVEC 2019 Workshop and Challe…Visually Interpretable Representation Learning for Depression Recognition from Facial Images (2018)Visually Interpretable Repres…Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features (2018)Video-Based Depression Level …Universals and cultural variations in 22 emotional expressions across five cultures. (2017)Universals and cultural varia…Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics (2017)Automated Depression Diagnosi…AVEC 2014 (2014)AVEC 2014Depression recognition based on dynamic facial and vocal expression features using partial least square regression (2013)Depression recognition based …AVEC 2013 (2013)AVEC 2013Development 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)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)Culture and the Self: Implications for Cognition, Emotion, and Motivation (1991)Emotion And Adaptation (1991)The weirdest people in the world? 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(2003)Analysing Affective Behavior in the First ABAW 2020 Competition (2020)A multimodal fusion model with multi-level attention mechanism for depression detection (2022)CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation (2022)Measurement Reliability and Validity (2000)A review of depression and suicide risk assessment using speech analysis (2015)A review of affective computing: From unimodal analysis to multimodal fusion (2017)A hierarchical depression detection model based on vocal and emotional cues (2021)Spatial–Temporal Attention Network for Depression Recognition from facial videos (2023)Estimating the reproducibility of psychological science (2015)Jupyter Notebooks – a publishing format for reproducible computational workflows (2016)A Transformer Architecture for Stress Detection from ECG (2021)Classification of Drivers' Workload Using Physiological Signals in Conditional Automation (2021)Depression Recognition Using Remote Photoplethysmography From Facial Videos (2023)A RATING SCALE FOR DEPRESSION (1960)The PHQ-9 (2001)Rethinking Rumination (2008)How COVID-19 shaped mental health: from infection to pandemic effects (2022)Cognitive behavior therapy vs. control conditions, other psychotherapies, pharmacotherapies and combined treatment for depression: a comprehensive meta‐analysis including 409 trials with 52,702 patients (2023)Global burden of depression or depressive symptoms in children and adolescents: A systematic review and meta-analysis (2024)Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017)Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2019)Natural language processing applied to mental illness detection: a narrative review (2022)
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References

  • 2021NeuroKit2: A Python toolbox for neurophysiological signal processing1,412 cited
  • 2022Time for united action on depression: a Lancet–World Psychiatric Association Commission1,100 cited
  • 2013AVEC 2013584 cited
  • 2014AVEC 2014475 cited
  • 2019AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition407 cited
  • 2017Universals and cultural variations in 22 emotional expressions across five cultures.343 cited
  • 2018Visually Interpretable Representation Learning for Depression Recognition from Facial Images273 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
  • 2020Multimodal Spatiotemporal Representation for Automatic Depression Level Detection172 cited
  • 2018Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features161 cited
  • 2020Spectral Representation of Behaviour Primitives for Depression Analysis149 cited
  • 2021MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection137 cited
  • 2023Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals

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137 cited
  • 2020A Deep Multiscale Spatiotemporal Network for Assessing Depression From Facial Dynamics116 cited
  • 2020Depression Level Prediction Using Deep Spatiotemporal Features and Multilayer Bi-LTSM110 cited
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