AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge

By Michel François Valstar, Jonathan Gratch, Björn Schuller, Fabien Ringeval, Denis Lalanne, M. Torres, Stefan Scherer, Giota Stratou, R. Cowie, Maja Pantic. AVEC@ACM Multimedia, 2016

679 citations recorded by the source corpus.

The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological 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 multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.

Subfields: Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7a55d39bc14a2745d2e603a6cb9534cc6272b77f

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Citations in the corpus

AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, background): “Concordance Correlation Coefficient (CCC) is another metric [40], [41] which combines the Pearsons correlation coefficient (CC) with the square difference between the means of two compared time series”
Multimodal sentiment analysis with word-level fusion and reinforcement learning (2017, methodology): “Multimodal models have been used for sentiment analysis [28, 45, 46]; medical purposes, such as detection of suicidal risk, PTSD and depression [29, 34, 35, 43]; emotion recognition [23]; image captioning and media description [8, 41]; question answering [2]; and multimodal translation [31].”
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018, methodology): “Before this and for two consecutive years (2015 [43], 2016 [55]) the RECOLA dataset was used.”
AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge (2017, methodology): “e competition measure is the concordance correlation coecient (CCC) [13], as previously used in the last two editions of AVEC [19, 29]; CCC evaluates the agreement between two time series by scaling their correlation coecient with their mean square dierence (1).”
End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017, background): “A part of this database was used for the Audio/Visual Emotion Challenge and Workshop (AVEC) 2016.”
Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017, background): “Nevertheless, AVEC uses only data captured in controlled conditions and under very specific scenarios.”
Facial Expression Analysis under Partial Occlusion (2018, background): “…and estimating the occurrence and intensity of AUs, the Audio/Visual Emotion Challenge and Workshop (AVEC) 2011-2016 [Ringeval et al. 2015],[Valstar et al. 2016], which focus on using continuous dimension representations, and the EmotiW 2013-2016 [Dhall et al. 2015c], [Dhall et al. 2016a]…”
Deep Facial Expression Recognition: A Survey (2018, neutral): “For example, participants in the EmotiW challenges and Audio Video Emotion Challenges (AVEC) [240], [241] considered the audio model to be the second most important element and employed various fusion techniques for multimodal FER.”
Deep learning in mental health outcome research: a scoping review (2020, background): “…to elicit facial expressions and speech responses of the subjects of bipolar disorder, unipolar depression, and healthy controls; 72 and (ii) the International Audio/Visual Emotion Recognition Challenges (AVEC) depression dataset 73 – 75 , collected within human – computer interaction scenario.”
Detecting Depression with Audio/Text Sequence Modeling of Interviews (2018, methodology): “Following the structure of the DAIC baseline [20], we reported the results of the development set for the binary classiﬁcation task using F1 score, precision, and recall.”
Tensor Fusion Network for Multimodal Sentiment Analysis (2017, background): “Using facial expressions and audio cues jointly has been the focus of many recent studies (Glodek et al., 2011; Valstar et al., 2016; Nojavanasghari et al., 2016).”