1 code implementation • 11 Jun 2024 • Shahin Amiriparian, Lukas Christ, Alexander Kathan, Maurice Gerczuk, Niklas Müller, Steffen Klug, Lukas Stappen, Andreas König, Erik Cambria, Björn Schuller, Simone Eulitz
The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems: In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of individuals such as assertiveness, dominance, likability, and sincerity based on the provided audio-visual data.
no code implementations • 15 May 2023 • Lukas Stappen, Jeremy Dillmann, Serena Striegel, Hans-Jörg Vögel, Nicolas Flores-Herr, Björn W. Schuller
This paper aims to serve as a comprehensive guide for researchers and practitioners, offering insights into the current state, potential applications, and future research directions for generative artificial intelligence and foundation models within the context of intelligent vehicles.
1 code implementation • 23 Jun 2022 • Lukas Christ, Shahin Amiriparian, Alice Baird, Panagiotis Tzirakis, Alexander Kathan, Niklas Müller, Lukas Stappen, Eva-Maria Meßner, Andreas König, Alan Cowen, Erik Cambria, Björn W. Schuller
For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset that contains audio-visual recordings of German football coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in which reactions of individuals to emotional stimuli have been annotated with respect to seven emotional expression intensities, and (iii) the Ulm-Trier Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled with continuous emotion values (arousal and valence) of people in stressful dispositions.
no code implementations • 17 Feb 2022 • Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton Batliner, Cecilia Mascolo, Björn W. Schuller
The COVID-19 pandemic has caused massive humanitarian and economic damage.
no code implementations • 27 Jul 2021 • Alice Baird, Lukas Stappen, Lukas Christ, Lea Schumann, Eva-Maria Meßner, Björn W. Schuller
We utilise a Long Short-Term Memory, Recurrent Neural Network to explore the benefit of fusing these physiological signals with arousal as the target, learning from various audio, video, and textual based features.
1 code implementation • 25 Jul 2021 • Lukas Stappen, Lea Schumann, Benjamin Sertolli, Alice Baird, Benjamin Weigel, Erik Cambria, Björn W. Schuller
With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards.
no code implementations • 4 May 2021 • Lukas Stappen, Alice Baird, Michelle Lienhart, Annalena Bätz, Björn Schuller
We investigate features extracted from these signals against various user engagement indicators including views, like/dislike ratio, as well as the sentiment of comments.
1 code implementation • 4 May 2021 • Lukas Stappen, Jason Thies, Gerhard Hagerer, Björn W. Schuller, Georg Groh
To unfold the tremendous amount of multimedia data uploaded daily to social media platforms, effective topic modeling techniques are needed.
1 code implementation • 14 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.
no code implementations • 24 Feb 2021 • Björn W. Schuller, Anton Batliner, Christian Bergler, Cecilia Mascolo, Jing Han, Iulia Lefter, Heysem Kaya, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Maurice Gerczuk, Panagiotis Tzirakis, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Leon J. M. Rothkrantz, Joeri Zwerts, Jelle Treep, Casper Kaandorp
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.
no code implementations • 15 Jan 2021 • Lukas Stappen, Alice Baird, Lea Schumann, Björn Schuller
Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research.
no code implementations • 15 Jun 2020 • Lukas Stappen, Xinchen Du, Vincent Karas, Stefan Müller, Björn W. Schuller
Systems for the automatic recognition and detection of automotive parts are crucial in several emerging research areas in the development of intelligent vehicles.
1 code implementation • 30 Apr 2020 • Lukas Stappen, Alice Baird, Georgios Rizos, Panagiotis Tzirakis, Xinchen Du, Felix Hafner, Lea Schumann, Adria Mallol-Ragolta, Björn W. Schuller, Iulia Lefter, Erik Cambria, Ioannis Kompatsiaris
Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition, as well as emotion-target engagement and trustworthiness detection by means of more comprehensively integrating the audio-visual and language modalities.
no code implementations • 13 Apr 2020 • Lukas Stappen, Fabian Brunn, Björn Schuller
Detecting hate speech, especially in low-resource languages, is a non-trivial challenge.
1 code implementation • 2 Dec 2019 • Zina Ibrahim, Honghan Wu, Ahmed Hamoud, Lukas Stappen, Richard Dobson, Andrea Agarossi
Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance in prognosis and treatment.