Workshops
Cross-Cultural Multimodal Interaction (CCMI)
Summary:
This workshop seeks to establish an international research platform to investigate the impact of linguistic and cultural differences on nonverbal behavior and their effects on communication dynamics. Moving beyond merely identifying nonverbal behavior patterns in specific cultural contexts, the workshop aims to uncover the mechanisms behind adaptation, change, and misunderstanding in intercultural interactions. The first year will focus on data-related challenges, such as collecting and annotating high-quality data across different regions. While advances in sensor technology, machine learning, and Large Language Models (LLMs) have been applied to linguistic diversity, their use in nonverbal communication remains underexplored. Given the known cultural variations in gestures, facial expressions, and turn-taking, integrating insights from humanities research with multimodal analysis is crucial. As LLMs continue to shape human-machine interactions globally, understanding and incorporating cultural differences in nonverbal behavior is an urgent and significant research challenge.
Webpage:
https://sites.google.com/view/ccmi2025/home
Organizers:
- Koji Inoue, Kyoto University, Japan
- Shogo Okada, Japan Advanced Institute of Science and Technology (JAIST), Japan
- Divesh Lala, Kyoto University, Japan
- Sahba Zojaji, The Chinese University of Hong Kong, Shenzhen, China
- Nancy F. Chen, Agency for Science, Technology, and Research (A*STAR), Singapore
- Tatsuya Kawahara, Kyoto University, Japan
Holistic and Responsible Affective Intelligence (HRAI)
Summary:
Affective computing techniques are typically developed for specific tasks in controlled settings, lacking the flexibility to handle multiple affective states simultaneously. Recently, foundation models have emerged as a promising solution, demonstrating strong performance across various affective tasks and offering a more comprehensive approach to affective intelligence. However, their adoption also raises critical ethical concerns, including privacy risks, fairness, sustainability, and bias. Therefore, ensuring their responsible and ethical use is more urgent than ever. This workshop aims to advance both the holistic development of affective computing and the understanding of its associated ethical challenges.
Webpage:
https://sites.google.com/view/hariworkshop
Organizers:
- Yuanchao Li, University of Edinburgh, UK
- Dimitrios Kollias, Queen Mary University London, UK
- Guillaume Chanel, University of Geneva , Switzerland
- Marios Fanourakis, University of Geneva , Switzerland
- Leimin Tian, CSIRO, Australia
- Madhawa Perera, Data61 CSIRO
- Michal Muszynski, IBM Research, Switzerland
- Brandon Booth, University of Memphis, USA
- Huili Chen, Princeton University, USA
- Catherine Lai, University of Edinburgh, UK
The Fifth International Workshop on Automated Assessment of Pain (AAP)
Summary:
Pain typically is measured by patient self-report, but self-reported pain is difficult to interpret and may be impaired or in some circumstances not possible to obtain. For instance, in patients with restricted verbal abilities such as neonates, young children, and in patients with certain neurological or psychiatric impairments (e.g., dementia). Additionally, the subjectively experienced pain may be partly or even completely unrelated to the somatic pathology of tissue damage and other disorders. Therefore, the standard self-assessment of pain does not always allow for an objective and reliable assessment of the quality and intensity of pain. Given individual differences among patients, their families, and healthcare providers, pain often is poorly assessed, underestimated, and inadequately treated. To improve assessment of pain, objective, valid, and efficient assessment of the onset, intensity, and pattern of occurrence of pain is necessary. To address these needs, several efforts have been made in the machine learning and computer vision communities for automatic and objective assessment of pain from video as a powerful alternative to self-reported pain. The workshop aims to bring together interdisciplinary researchers working in the field of automatic multimodal assessment of pain (using video and physiological signals). A key focus of the workshop is the translation of laboratory work into clinical practice.
Webpage:
Challenge:
https://sites.google.com/view/ai4pain2025/home
Organizers:
- Zakia Hammal, The Robotics Institute, Carnegie Mellon University, USA.
- Raul Fernandez-Rojas, University of Canberra, Australia.
- Steffen Walter, University Hospital Ulm, Germany.
- Nadia Berthouze, University College London, UK.
- Roland Goecke, University of Canberra, Australia.
- Ben Seymour, University of Oxford, UK.