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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.<\/span><\/p>\n 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.<\/span><\/p>\n https:\/\/sites.google.com\/view\/hariworkshop<\/span><\/a><\/p>\n 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.<\/span><\/p>\n http:\/\/aap-workshop.net<\/span><\/a>\u00a0<\/span><\/p>\nWebpage:\u00a0<\/b><\/h5>\n
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Holistic and Responsible Affective Intelligence (HRAI)<\/b><\/h4>\n
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The Fifth International Workshop on Automated Assessment of Pain (AAP)<\/b><\/h4>\n
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