A novel transformer autoencoder for multi-modal emotion recognition with incomplete data

Multi-modal signals have become essential data for emotion recognition since they can represent emotions more comprehensively. However, in real-world environments, it is often impossible to acquire complete data on multi-modal signals, and the problem of missing modalities causes severe performance...

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Bibliographic Details

Published in:	Neural networks Vol. 172; p. 106111
Main Authors:	Cheng, Cheng, Liu, Wenzhe, Fan, Zhaoxin, Feng, Lin, Jia, Ziyu
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.04.2024
Subjects:	Convolutional encoder Emotion recognition Incomplete data Multi-modal signals Transformer autoencoder Emotion recognition Incomplete data Multi-modal signals Convolutional encoder Transformer autoencoder
ISSN:	0893-6080, 1879-2782, 1879-2782
Online Access:	Get full text
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