UF-AMA: A unified framework for cross-domain emotion recognition via adaptive multimodal alignment
In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions. However, due to distribution shifts caused by individual and contextual differences, along with variations in sample quality across modalities, constructing a cross-domain multimodal emotion recognition model with high generalization and robustness remains a key challenge. In this study, we propose a Unified Framework with Adaptive Multimodal Alignment (UF-AMA) to address cross-subject and cross-session emotion recogniti...
| Authors | Zheng Wang, Shuo Wang, Junhong Wang |
|---|---|
| Primary category | cs.HC |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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