SPECIAL session 5

Chair:

Zhuhong Shao, Capital Normal University, China | Email: zhshao@cnu.edu.cn

Co-chairs:
Mingyue Niu, Yanshan University, China | Email: niumingyue@ysu.edu.cn
Wentao Xiang, Nanjing Medical University, China | Email: xiangbmu@njmu.edu.cn
Zongya Zhao, Henan Medical University, China | Email: zhaozongya@xxmu.edu.cn
Bicao Li, Zhongyuan University of Technology, China | Email: lbc@zut.edu.cn

Submission Link: http://www.easychair.org/conferences/?conf=icivc2026 (Select Track Special Session 5)

Introduction:

Multimodal intelligence has become a pivotal driving force in emotion recognition and health perception. This session, themed “Multimodal Intelligence for Emotion Recognition and Health Perception”, is a dedicated platform for global experts to exchange cutting-edge research and innovative achievements. This session invites papers focusing on technical breakthroughs, practical application case studies, and ethical and privacy considerations across these themes. Discussions will center on integrating multimodal intelligence with core technologies of emotion recognition and health perception to explore new application scenarios, while we also welcome submissions on multimodal data fusion, lightweight model optimization and cross-domain adaptation strategies. It aims to facilitate in-depth knowledge exchange, accelerate the transformation of research outcomes, and chart the future development of multimodal intelligence in emotion and health perception fields.

Call for topic:
Fusion architectures for emotion recognition and health perception
Physiological signal processing for intelligent health monitoring
Lightweight edge models for emotion and health computing
Cross-domain & cross-population adaptation in affective perception
Machine learning for mental health and stress detection
Real-time intelligent systems for emotion and health perception
Privacy and interpretability in affective & health intelligence
Intelligent perception for healthcare and human-computer interaction
Benchmarks and evaluation for affective health systems
Large model enhancement for emotion understanding and health prediction