<ul><li>Multimodal sentiment analysis is crucial for understanding human emotions across various communication channels.</li><li>A novel framework called Hierarchical Adaptive Expert for Multimodal Sentiment Analysis (HAEMSA) is proposed to address the challenge of effectively integrating modality-shared and modality-specific information.</li><li>HAEMSA utilizes an evolutionary optimization approach, cross-modal knowledge transfer, and multi-task learning to capture both global and local modality representations.</li><li>Extensive experiments demonstrate that HAEMSA outperforms previous methods, achieving improved accuracy and reducing mean absolute error on multiple benchmark datasets.</li></ul>

Hierarchical Adaptive Expert for Multimodal Sentiment Analysis

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