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Disentangled Representation and Contrastive Learning with Adaptive Affinity Squeeze-Excitation for Multimodal Emotion Recognition.

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Titel: Disentangled Representation and Contrastive Learning with Adaptive Affinity Squeeze-Excitation for Multimodal Emotion Recognition.
Autoren: Wang, Jhing-Fa1,2 (AUTHOR), Tsai, Hsin-Chun3 (AUTHOR) tsaihcmail@gmail.com, Liu, Jie-Ming1 (AUTHOR)
Quelle: International Journal of Pattern Recognition & Artificial Intelligence. Nov2025, p1. 20p. 2 Illustrations.
Schlagwörter: EMOTION recognition, FEATURE extraction
Abstract: Multimodal Emotion Recognition (MER) aims to leverage information from multiple modalities to enhance the accuracy of emotion classification. However, existing methods struggle with properly disentangling shared and modality-specific features while maintaining discriminative power across modalities. To address this, we propose a novel contrastive learning-based MER framework that enforces inter-modal alignment while preserving intra-modal distinctiveness. Our method employs disentangled representation learning to extract both shared and private representations, ensuring that complementary features are effectively utilized. Additionally, we introduce an Adaptive Affinity Squeeze-Excitation (AASE) mechanism to dynamically refine multimodal representations by capturing inter-channel relationships, further enhancing the model’s ability to identify crucial emotional cues. Experimental results demonstrate that our method improves emotion classification accuracy to 84.6% on benchmark datasets. Accordingly, the proposed MER framework provides a robust and highly adaptive solution for multimodal emotion analysis, effectively integrating modality-specific features while enhancing discriminability across modalities. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
Abstract:Multimodal Emotion Recognition (MER) aims to leverage information from multiple modalities to enhance the accuracy of emotion classification. However, existing methods struggle with properly disentangling shared and modality-specific features while maintaining discriminative power across modalities. To address this, we propose a novel contrastive learning-based MER framework that enforces inter-modal alignment while preserving intra-modal distinctiveness. Our method employs disentangled representation learning to extract both shared and private representations, ensuring that complementary features are effectively utilized. Additionally, we introduce an Adaptive Affinity Squeeze-Excitation (AASE) mechanism to dynamically refine multimodal representations by capturing inter-channel relationships, further enhancing the model’s ability to identify crucial emotional cues. Experimental results demonstrate that our method improves emotion classification accuracy to 84.6% on benchmark datasets. Accordingly, the proposed MER framework provides a robust and highly adaptive solution for multimodal emotion analysis, effectively integrating modality-specific features while enhancing discriminability across modalities. [ABSTRACT FROM AUTHOR]
ISSN:02180014
DOI:10.1142/s0218001425510231