Sequence Modeling and Feature Fusion for Multimodal Emotion Recognition-Reference-Cited by-同舟云学术

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Sequence Modeling and Feature Fusion for Multimodal Emotion Recognition

Published:2024-04-26 Issue: Volume: Page:831-835
ISSN:
Container-title:2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)
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Author:

Li Xiang¹,Feng Ruoran²,Yue Hao¹,Zang Haozhi¹,Yang Qi³

Affiliation:

1. College of Computer Science and Technology, Shandong Jianzhu University,Jinan,China

2. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine,Jinan,China

3. School of Physics and Electronic Information, Yantai University,Yantai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10545644/10545608/10546216.pdf?arnumber=10546216

Reference10 articles.

1. Conversational emotion recognition studies based on graph convolutional neural networks and a dependent syntactic analysis

2. A multi-message passing framework based on heterogeneous graphs in conversational emotion recognition

3. A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition[J];Shou;arXiv preprint,2023

4. A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning[J];Shou;arXiv preprint,2023

5. Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations[J];Meng;arXiv preprint,2023

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