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

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.