Search Results for author: Eva-Maria Messner

Found 4 papers, 1 papers with code

The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress

1 code implementation14 Apr 2021 Lukas Stappen, Alice Baird, Lukas Christ, Lea Schumann, Benjamin Sertolli, Eva-Maria Messner, Erik Cambria, Guoying Zhao, Björn W. Schuller

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities.

Emotion Recognition Multimodal Sentiment Analysis

Annotation of Emotion Carriers in Personal Narratives

no code implementations LREC 2020 Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner, Giuseppe Riccardi

We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts.

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

no code implementations10 Jul 2019 Fabien Ringeval, Björn Schuller, Michel Valstar, NIcholas Cummins, Roddy Cowie, Leili Tavabi, Maximilian Schmitt, Sina Alisamir, Shahin Amiriparian, Eva-Maria Messner, Siyang Song, Shuo Liu, Ziping Zhao, Adria Mallol-Ragolta, Zhao Ren, Mohammad Soleymani, Maja Pantic

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.

Emotion Recognition

Modeling user context for valence prediction from narratives

no code implementations9 May 2019 Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner, Giuseppe Riccardi

Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e. g. early diagnosis of mental diseases, supervision of disease course, etc.).

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