Emotional Reaction Intensity
Emotional reaction intensity (ERI) research focuses on accurately measuring the strength of emotional responses to stimuli, primarily using multimodal data (audio and video). Current research employs sophisticated deep learning architectures, such as transformers and recurrent neural networks, often incorporating spatial and temporal attention mechanisms to effectively process and fuse information from different modalities. These advancements aim to improve the accuracy and robustness of ERI estimation, with applications in fields like healthcare, human-computer interaction, and affective computing, ultimately leading to a better understanding of human emotion.
Papers
A Dual Branch Network for Emotional Reaction Intensity Estimation
Jun Yu, Jichao Zhu, Wangyuan Zhu, Zhongpeng Cai, Guochen Xie, Renda Li, Gongpeng Zhao
Emotional Reaction Intensity Estimation Based on Multimodal Data
Shangfei Wang, Jiaqiang Wu, Feiyi Zheng, Xin Li, Xuewei Li, Suwen Wang, Yi Wu, Yanan Chang, Xiangyu Miao
Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers
Jia Li, Yin Chen, Xuesong Zhang, Jiantao Nie, Ziqiang Li, Yangchen Yu, Yan Zhang, Richang Hong, Meng Wang
Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challenge
Ziyang Zhang, Liuwei An, Zishun Cui, Ao xu, Tengteng Dong, Yueqi Jiang, Jingyi Shi, Xin Liu, Xiao Sun, Meng Wang