AVEC 2017 - Real-life depression, and affect recognition workshop and challenge

Fabien Ringeval, Jonathan Gratch, Sharon Mozgai, Bjorn Schuller, Roddy Cowie, Nicholas Cummins, Maja Pantic, Michel Valstar, Stefan Scherer, Maximilian Schmitt

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

259 Scopus citations

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).

Original languageEnglish
Title of host publicationAVEC 2017 - Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, co-located with MM 2017
PublisherAssociation for Computing Machinery, Inc
Pages3-9
Number of pages7
ISBN (Electronic)9781450355025
DOIs
StatePublished - 23 Oct 2017
Externally publishedYes
Event7th Annual Workshop on Audio/Visual Emotion Challenge, AVEC 2017 - Mountain View, United States
Duration: 23 Oct 2017 → …

Publication series

NameAVEC 2017 - Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, co-located with MM 2017

Conference

Conference7th Annual Workshop on Audio/Visual Emotion Challenge, AVEC 2017
Country/TerritoryUnited States
CityMountain View
Period23/10/17 → …

Keywords

  • Affective computing
  • Automatic emotion/depression recognition
  • Social signal processing

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