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

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

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

    106 Citations (Scopus)

    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'17
    Subtitle of host publicationProceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge
    PublisherAssociation for Computing Machinery (ACM)
    Pages3-9
    Number of pages7
    ISBN (Electronic)978-1-4503-5502-5
    DOIs
    Publication statusPublished - 23 Oct 2017
    Event7th Audio/Visual Emotion Challenge and Workshop: Real-life Depression and Affect - Mountain View, United States
    Duration: 23 Oct 201723 Oct 2017
    Conference number: 7

    Conference

    Conference7th Audio/Visual Emotion Challenge and Workshop
    Abbreviated titleAVEC 2017
    CountryUnited States
    CityMountain View
    Period23/10/1723/10/17

    Keywords

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

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