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The EU H2020 project social cohesion, participation, and inclusion through cultural engagement (SPICE) focuses on developing, designing, and implementing new methods and digital tools for citizen curation. This article delineates several software tools ...

Biosensing technologies are on their way to becoming ubiquitous in multimedia interaction. These technologies capture physiological data, such as heart rate, breathing, skin conductance, and brain activity. Researchers are exploring biosensing from ...

What should we do with emotion AI? Should we regulate, ban, promote, or re-imagine it? Emotion AI, a class of affective computing technologies used in personal and social computing, comprises emergent and controversial techniques aiming to classify human ...

The rapid advancements in generative AI have spurred the development of AI chatbots for emotional support. In this work, we designed ArtTheraCat, a novel multimodal chatbot that promotes mental health by integrating supportive dialogue with artistic ...

Emotion AI is increasingly used to automatically evaluate asynchronous hiring interviews. Although touted for increasing hiring fit and reducing bias, it is unclear how job-seekers perceive emotion AI-enabled asynchronous interviews. This gap is striking,...

Video-based learning has become a popular, scalable, and effective approach for students to learn new skills. Many of the challenges for video-based learning can be addressed with machine learning models. However, the available datasets often lack the ...

Stress detection in real-world settings presents significant challenges due to the complexity of human emotional expression influenced by biological, psychological, and social factors. While traditional methods like EEG, ECG, and EDA sensors provide ...

MRAC'24 is the continuation of last year's MRAC'23. The main goal of this workshop is to promote the application of affective computing technology in real-world scenarios. We have equipped this workshop with MER'24 Challenge, which provides a platform ...

With the rapid advancements in multimodal generative technology, Affective Computing research has provoked discussion about the potential consequences of AI systems equipped with emotional intelligence. Affective Computing involves the design, evaluation,...

Spotting people's expressions is pivotal, as it directly reflects emotions, particularly those underlying feelings and intentions that may not be expressed verbally. Therefore, detecting macro- and micro-expressions play a critical role in psychological ...

Robotics has made significant progress in cases of structured and constrained environments, e.g., manufacturing. However, it is still in its infancy when it comes to applications in unstructured and unconstrained situations e.g., social environments. In ...

The 5th Multimodal Sentiment Analysis Challenge (MuSe), a workshop in conjunction with ACM Multimedia '24, is focused on Multimodal Machine Learning in the domain of Affective Computing. Two different sub-challenges are proposed: Social Perception Sub-...

The parallel audio-visual-text data contains vast amount of information. Thus it is essential to develop machine learning algorithms that can utilise them efficiently. In this work, we investigated unimodal and multimodal solutions for MuSe Humor and ...

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 ...

The Multimodal Sentiment Analysis Challenge presents two distinct sub-challenges related to human perception characteristics. This paper focuses on the MUSE-PERCEPTION challenge, which aims to predict the perceptual attributes of CEOs from video data. ...

Designing reliable automatic models for social perception can contribute to a better understanding of human behavior, enabling more trustworthy experiences in the multimedia on-line communication environment. However, predicting social attributes from ...

The integration of human emotions into multimedia applications shows great potential for enriching user experiences and enhancing engagement across various digital platforms. Unlike traditional methods such as questionnaires, facial expressions, and ...

Emotion and sentiment analysis (ESA) assists machines to serve humans more intelligently. However, collecting large-scale high-quality datasets for training ESA models in a supervised manner is expensive, time-consuming, and difficult in practice. This ...

Engagement estimation is crucial for advancing natural human-computer interaction, allowing artificial agents to dynamically adjust their responses based on user engagement levels and creating more intuitive and immersive experiences. Despite ...

Due to the small size of valid samples, multi-source EEG features with high dimensionality can easily cause problems such as overfitting and poor real-time performance of the emotion recognition classifier. Feature selection has been demonstrated as an ...