research-article

AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge

Authors:

Fabien Ringeval,

Björn Schuller,

Michel Valstar,

Jonathan Gratch,

Stefan Scherer,

Nicholas Cummins,

Maximilian Schmitt,

Maja PanticAuthors Info & Claims

AVEC '17: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge

Pages 3 - 9

https://doi.org/10.1145/3133944.3133953

Published: 23 October 2017 Publication History

Abstract

The Audio/Visual Emotion Challenge and Workshop (AVEC 2017) "Real-life depression, and affect" will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the depression and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of the various approaches to depression and emotion recognition from real-life data. This paper presents the novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline system on the two proposed tasks: dimensional emotion recognition (time and value-continuous), and dimensional depression estimation (value-continuous).

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Cited By

Bauer JGerczuk MSchindler-Gmelch LAmiriparian SEbert DKrajewski JSchuller BBerking M(2024)Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine CareDepression and Anxiety10.1155/2024/96673772024(1-12)Online publication date: 9-Apr-2024
https://doi.org/10.1155/2024/9667377
Ringeval FSchuller BBailly GAzzakhnini SFournier H(2024)EVAC 2024 – Empathic Virtual Agent Challenge: Appraisal-based Recognition of Affective StatesProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3689029(677-683)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3689029
Pan YJiang JJiang KLiu XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Disentangled-Multimodal Privileged Knowledge Distillation for Depression Recognition with Incomplete Multimodal DataProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681227(5712-5721)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681227
Show More Cited By

Index Terms

AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics
2. General and reference
  1. Cross-computing tools and techniques
    1. Performance

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Information

Published In

cover image ACM Conferences

AVEC '17: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge

October 2017

78 pages

ISBN:9781450355025

DOI:10.1145/3133944

General Chairs:
Fabien Ringeval
University of Grenoble Alpes
,
Björn Schuller
University of Passau/Imperial College London
,
Michel Valstar
University of Nottingham
,
Jonathan Gratch
University of Southern California
,
Roddy Cowie
Queen's University Belfast
,
Maja Pantic
Imperial College London/Twente University, UK/The Netherlands

Copyright © 2017 ACM.

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2017

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Funding Sources

Horizon 2020 Programme
European Union's 7th Framework Programme
Research Innovative Action

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MM '17

Sponsor:

SIGMM

MM '17: ACM Multimedia Conference

October 23, 2017

California, Mountain View, USA

Acceptance Rates

AVEC '17 Paper Acceptance Rate 8 of 17 submissions, 47%;

Overall Acceptance Rate 52 of 98 submissions, 53%

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Cited By

Bauer JGerczuk MSchindler-Gmelch LAmiriparian SEbert DKrajewski JSchuller BBerking M(2024)Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine CareDepression and Anxiety10.1155/2024/96673772024(1-12)Online publication date: 9-Apr-2024
https://doi.org/10.1155/2024/9667377
Ringeval FSchuller BBailly GAzzakhnini SFournier H(2024)EVAC 2024 – Empathic Virtual Agent Challenge: Appraisal-based Recognition of Affective StatesProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3689029(677-683)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3689029
Pan YJiang JJiang KLiu XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Disentangled-Multimodal Privileged Knowledge Distillation for Depression Recognition with Incomplete Multimodal DataProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681227(5712-5721)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681227
Song SLuo YTumer TFu CValstar MGunes H(2024)Loss Relaxation Strategy for Noisy Facial Video-based Automatic Depression RecognitionACM Transactions on Computing for Healthcare10.1145/3648696Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3648696
Liu XLin JWang C(2024)Graph-Based Multi-Feature Fusion Method for Speech Emotion RecognitionInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142450023XOnline publication date: 16-Dec-2024
https://doi.org/10.1142/S021800142450023X
Han ZShang YShao ZLiu JGuo GLiu TDing HHu Q(2024)Spatial–Temporal Feature Network for Speech-Based Depression RecognitionIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2023.327361416:1(308-318)Online publication date: Feb-2024
https://doi.org/10.1109/TCDS.2023.3273614
Huang GLi JLu HGuo MChen S(2024)Rethinking Inconsistent Context and Imbalanced Regression in Depression Severity PredictionIEEE Transactions on Affective Computing10.1109/TAFFC.2024.340558415:4(2154-2168)Online publication date: 27-May-2024
https://dl.acm.org/doi/10.1109/TAFFC.2024.3405584
Ye JYu YZheng YLiu YWang Q(2024)Dep-FER: Facial Expression Recognition in Depressed Patients Based on Voluntary Facial Expression MimicryIEEE Transactions on Affective Computing10.1109/TAFFC.2024.337010315:3(1725-1738)Online publication date: Jul-2024
https://doi.org/10.1109/TAFFC.2024.3370103
Pan YShang YShao ZLiu TGuo GDing H(2024)Integrating Deep Facial Priors Into Landmarks for Privacy Preserving Multimodal Depression RecognitionIEEE Transactions on Affective Computing10.1109/TAFFC.2023.329631815:3(828-836)Online publication date: Jul-2024
https://doi.org/10.1109/TAFFC.2023.3296318
Niu MTao JLi YQin YLi Y(2024)WavDepressionNet: Automatic Depression Level Prediction via Raw Speech SignalsIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327255315:1(285-296)Online publication date: Jan-2024
https://doi.org/10.1109/TAFFC.2023.3272553
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