兰州大学机构库
MD-EPN: An Efficient Multimodal Emotion Recognition Method Based on Multi-Dimensional Feature Fusion
K. Xie; Z. Xie
2024-04-22
Source Publication2024 4th International Conference on Neural Networks, Information and Communication (NNICE)   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
Conference Name4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024
Conference DateJanuary 19, 2024 - January 21, 2024
Pages613-618
AbstractMultimodal emotion recognition is extensively applied in diverse areas including driving monitoring, online education, telemedicine, and customer service, marking it as a significant technology in contemporary research. Traditional emotion recognition methods predominantly concentrate on unimodal data processing, which can lead to low accuracy and substantial information loss. In response to these issues, this paper introduces an innovative Multi-Dimensional Emotion Perception Network (MD-EPN) that leverages advanced technologies to integrate textual, auditory, and visual data, thereby aiming to enhance the overall comprehensiveness and precision of emotion recognition. By focusing optimizations on crucial dimensions, the proposed architecture not only bolsters the model's performance but also strikes an effective balance between computational efficiency and accuracy retention, exhibiting exemplary emotion classification accuracy on the CMU-MOSI dataset.
Keywordcomponent: Emotion Recognition Multimodal Fusion Multi-dimensional Feature Fusion Modality Integration Strategies Artificial Intelligence
PublisherIEEE
DOI10.1109/NNICE61279.2024.10498191
Indexed ByIEEE ; EI
Language英语
Funding OrganizationIEEE
EI Accession Number20242016076805
EI KeywordsEmotion Recognition
EI Classification Number461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 751.5 Speech ; 903.1 Information Sources and Analysis ; 971 Social Sciences
Original Document TypeConference article (CA)
Conference PlaceHybrid, Guangzhou, China
Citation statistics
Document Type会议论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/588984
Collection兰州大学
信息科学与工程学院
Affiliation
1.School of Information Science & Engineering, Lanzhou University, Lanzhou, China
2.School of Information Science & Engineering, Lanzhou University, Lanzhou, China
Recommended Citation
GB/T 7714
K. Xie,Z. Xie. MD-EPN: An Efficient Multimodal Emotion Recognition Method Based on Multi-Dimensional Feature Fusion[C],2024:613-618.
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