Learning Better Representations for Audio-Visual Emotion Recognition with Common Information
Audio-visual emotion recognition aims to distinguish human emotional states by integrating the audio and visual data acquired in the expression of emotions. It is crucial for facilitating the affect-related human-machine interaction system by enabling machines to intelligently respond to human emoti...
Main Authors: | Fei Ma, Wei Zhang, Yang Li, Shao-Lun Huang, Lin Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-10-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/20/7239 |
Similar Items
-
Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
by: Fei Ma, et al.
Published: (2022-01-01) -
A Maximal Correlation Framework for Fair Machine Learning
by: Joshua Lee, et al.
Published: (2022-03-01) -
A Neural Network Architecture for Children’s Audio–Visual Emotion Recognition
by: Anton Matveev, et al.
Published: (2023-11-01) -
Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration
by: Yong Peng, et al.
Published: (2023-01-01) -
Maximal Correlation Regression
by: Xiangxiang Xu, et al.
Published: (2020-01-01)