标题： MVP：基于视频和生理信号的多模态情感识别 显示英文标题

标题： MVP: Multimodal Emotion Recognition based on Video and Physiological Signals

摘要： 人类情绪涉及行为、生理和认知变化的复杂集合。 当前最先进的模型使用经典机器学习融合行为和生理成分，而不是最近的深度学习技术。 我们提出填补这一空白，设计了用于视频和生理信号的多模态(MVP)架构，该架构简化以融合视频和生理信号。 与其他方法不同，MVP利用注意力的优势，以支持使用长输入序列（1-2分钟）。 我们研究了用于输入长序列的视频和生理主干，并根据最新技术评估了我们的方法。 我们的结果表明，MVP在基于面部视频、EDA和ECG/PPG的情绪识别方面优于以前的方法。 显示英文摘要

摘要： Human emotions entail a complex set of behavioral, physiological and cognitive changes. Current state-of-the-art models fuse the behavioral and physiological components using classic machine learning, rather than recent deep learning techniques. We propose to fill this gap, designing the Multimodal for Video and Physio (MVP) architecture, streamlined to fuse video and physiological signals. Differently then others approaches, MVP exploits the benefits of attention to enable the use of long input sequences (1-2 minutes). We have studied video and physiological backbones for inputting long sequences and evaluated our method with respect to the state-of-the-art. Our results show that MVP outperforms former methods for emotion recognition based on facial videos, EDA, and ECG/PPG.