AVEC 2012: the continuous audio/visual emotion challenge

By Björn Schuller, Michel François Valstar, Florian Eyben, R. Cowie, Maja Pantic. International Conference on Multimodal Interaction, 2012

305 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e653f5024773a5bc079e72a862de5a5af784ff33

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In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments (2016, background): “…6 voicing-related LLDs with 32 functionals, 25 delta coefficients of energy/spectral-related LLDs with 23 functionals, 6 delta coefficients of voicing-related LLDs with 19 functionals, and 10 voiced/unvoiced durational features (for full details on the feature set please refer to [39]).”
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations (2018, methodology): “We use two emotion detection datasets IEMOCAP (Busso et al. 2008) and AVEC (Schuller et al. 2012) to evaluate Dia-logueRNN.”
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (2019, methodology): “(Schuller et al., 2012) dataset is a modiﬁcation of SEMAINE database (McKeown et al., 2012) containing interactions between humans and artiﬁcially intelligent agents.”
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 binarizing its continuous values, while the second challenge [49] (2012) used the same database but with its original values.”
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”
Directed Acyclic Graph Network for Conversational Emotion Recognition (2021, background): “Recently, several ERC datasets with textual data have been released (Busso et al., 2008; Schuller et al., 2012; Zahiri and Choi, 2017; Li et al., 2017; Chen et al., 2018; Poria et al., 2019b), arousing the widespread interest of NLP researchers.”
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation (2021, background): “With the fast development of social media, much more interaction data become available, including several open-sourced conversation datasets such as IEMOCAP(Busso et al., 2008), AVEC(Schuller et al., 2012), MELD(Poria et al., 2018), etc. ERC has attracted much research attention recently.”
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (2018, methodology): “Speciﬁcally, we utilize the AVEC 2012’s fully continuous sub-challenge (Schuller et al., 2012) that requires predictions of four continuous affective dimensions: arousal, expectancy, power, and valence.”
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019, background): “cessing and machine learning methods for automatic audio, visual, and audiovisual health and emotion sensing, with all participants competing strictly under the same conditions [48–50, 62, 63, 72, 74, 75].”
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (2018, methodology): “Both IEMOCAP(Busso et al., 2008) and SEMAINE (Schuller et al., 2012) contain only dyadic conversations wherein the dialogues in MELD are multiparty.”
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition (2015, methodology): “For instance, none of the FERA, AVEC'11/'12 participants relied on spatio-temporal representations.”