Hierarchical Adaptive Expert for Multimodal Sentiment Analysis
This addresses the challenge of limited performance in multimodal learning for sentiment analysis, though it appears incremental as it builds on existing methods with specific gains.
The paper tackles the problem of effectively differentiating and integrating modality-shared and modality-specific information in multimodal sentiment analysis, resulting in HAEMSA achieving improvements such as a 2.6% increase in 7-class accuracy on CMU-MOSEI and a 6.3% improvement on CMU-MOSI.
Multimodal sentiment analysis has emerged as a critical tool for understanding human emotions across diverse communication channels. While existing methods have made significant strides, they often struggle to effectively differentiate and integrate modality-shared and modality-specific information, limiting the performance of multimodal learning. To address this challenge, we propose the Hierarchical Adaptive Expert for Multimodal Sentiment Analysis (HAEMSA), a novel framework that synergistically combines evolutionary optimization, cross-modal knowledge transfer, and multi-task learning. HAEMSA employs a hierarchical structure of adaptive experts to capture both global and local modality representations, enabling more nuanced sentiment analysis. Our approach leverages evolutionary algorithms to dynamically optimize network architectures and modality combinations, adapting to both partial and full modality scenarios. Extensive experiments demonstrate HAEMSA's superior performance across multiple benchmark datasets. On CMU-MOSEI, HAEMSA achieves a 2.6% increase in 7-class accuracy and a 0.059 decrease in MAE compared to the previous best method. For CMU-MOSI, we observe a 6.3% improvement in 7-class accuracy and a 0.058 reduction in MAE. On IEMOCAP, HAEMSA outperforms the state-of-the-art by 2.84% in weighted-F1 score for emotion recognition. These results underscore HAEMSA's effectiveness in capturing complex multimodal interactions and generalizing across different emotional contexts.