Multimodal Knowledge Distillation for Emotion Recognition
Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. Among various physiological signals, EEG signals and EOG data are highly valued for their complementary strengths in emotion recognition. However...
Saved in:
| Main Authors: | Zhenxuan Zhang, Guanyu Lu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Brain Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3425/15/7/707 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Optimised knowledge distillation for efficient social media emotion recognition using DistilBERT and ALBERT
by: Muhammad Hussain, et al.
Published: (2025-08-01) -
EEG-SKDNet: A Self-Knowledge Distillation Model With Scaled Weights for Emotion Recognition From EEG Signals
by: Thuong Duong Thi Mai, et al.
Published: (2025-01-01) -
Shuffling Augmented Decoupled Features for Multimodal Emotion Recognition
by: Sunyoung Cho
Published: (2025-01-01) -
Af-CAN: Multimodal Emotion Recognition Method Based on Situational Attention Mechanism
by: Xue Zhang, et al.
Published: (2025-01-01) -
Intelligent emotion recognition for drivers using model-level multimodal fusion
by: Xing Luan, et al.
Published: (2025-07-01)