ArticleSensors (Basel, Switzerland)2025
Multimodal Emotion Recognition Using Modality-Wise Knowledge Distillation.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Closed-loop artificial intelligence agents for animal epidemic prediction and decision support in livestock farming: a review.Frontiers in veterinary science · 2026Review
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Authors and funding
3 authors.
Funding
Abstract
Multimodal emotion recognition (MER) aims to estimate emotional states utilizing multiple sensors simultaneously. Most previous MER models extract unimodal representation via modality-wise encoders and combine them into a multimodal representation to classify the emotion, and these models are trained with an objective for the final output of the MER. If an encoder for a specific modality is optimized better than others at some point of the training procedure, the parameters for the other encoders may not be sufficiently updated to provide optimal performance. In this paper, we propose a MER using modality-wise knowledge distillation, which adapts the unimodal encoders using pre-trained unimodal emotion recognition models. Experimental results on CREMA-D and IEMOCAP databases demonstrated that the proposed method outperformed previous approaches to overcome the optimization imbalance phenomenon and could also be combined with these approaches effectively.
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Registered trials
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