AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
By Fabien Ringeval, Björn Schuller, Michel François Valstar, Nicholas Cummins, R. Cowie, Leili Tavabi, Maximilian Schmitt, Sina Alisamir, Shahin Amiriparian, Eva-Maria Messner and 7 more. AVEC@MM, 2019
420 citations recorded by the source corpus.
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) 'State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition' is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under 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 health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.
Subfields: Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/db6c65844e4e9085d712d25ae156b7941ab21594
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In Experimental and Cognitive Psychology
Citations in the corpus
- Analysing Affective Behavior in the First ABAW 2020 Competition (2020, positive): “The Concordance Correlation Coefficient (CCC) is widely used in measuring the performance of dimensional emotion recognition methods, such as in the series of AVEC challenges [30].”