AVEC 2011-The First International Audio/Visual Emotion Challenge
By Björn Schuller, Michel François Valstar, Florian Eyben, Gary McKeown, R. Cowie, Maja Pantic. Affective Computing and Intelligent Interaction, 2011
314 citations recorded by the source corpus.
Subfields: Experimental and Cognitive Psychology, Signal Processing
View Paper: https://www.semanticscholar.org/paper/27daf5bb692f8fba491f349ab11821e59f974c72
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Experimental and Cognitive Psychology
- Maja Pantic
- Stefanos P. Zafeiriou
- Michel François Valstar
- Dimitrios Kollias
- Tadas Baltrusaitis
- Björn Schuller
- Ali Mollahosseini
- Peter Robinson
In Signal Processing
- Maja Pantic
- Stefanos P. Zafeiriou
- Tadas Baltrusaitis
- Michel François Valstar
- Björn Schuller
- Mihalis A. Nicolaou
- Peter Robinson
- Irene Kotsia
Citations in the corpus
- Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018, methodology): “The first challenge [48] (2011) used the SEMAINE database for classification purposes by”
- Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015 (2015, methodology): “The parameters are: range = [0-360], pyramid levels = 3 and number of bins = 16.”
- A Review of Human Activity Recognition Methods (2015, background): “Siddiquie et al. (2013) analyzed four different affective dimensions, such as activation, expectancy, power, and valence (Schuller et al., 2011).”
- A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “Affective recognition can be divided into classification (emotions, dimensional or polarity) and continuous dimensional regression (Pleasure, Arousal, Dominance Expectation, or Intensity) [413,414].”
- Meta-Analysis of the First Facial Expression Recognition Challenge (2012, background): “Recently, there has been more interest in the automatic recognition of dimensional affect [22], [23], [46].”
- Recurrent Neural Networks for Emotion Recognition in Video (2015, methodology): “These are based on the approach from [27].”
- Cross-dataset learning and person-specific normalisation for automatic Action Unit detection (2015, background): “The following sections describe our features in more details.”
- AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, background): “Pearsons correlation coefficient is therefore proposed in some literature [24], [36], [37] to overcome this limitation”