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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Vydáno v:	Neural networks Ročník 172; s. 106111
Hlavní autoři:	Cheng, Cheng, Liu, Wenzhe, Fan, Zhaoxin, Feng, Lin, Jia, Ziyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.04.2024
Témata:	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
On-line přístup:	Získat plný text
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