跨模态特征增强与层次化MLP通信的多模态情感分析OA

In multimodal sentiment analysis tasks,effective fusion of sentiment information between different modalities is hindered due to three challenges:nonverbal modal information being insufficiently utilized,fine-grained associative modeling for cross-modal interactions being inadequately established,and hierarchical semantic fusion mechanisms being imperfectly designed.To address these issues,a multimodal sentiment analysis method with cross-modal feature enhancement and hierarchical MLP communication is proposed in this paper.A progressive fusion architecture is constructed,where nonverbal modal information is first enhanced through a cross-modal attention mechanism,enabling many-to-many cross-modal fine-grained interactions to be captured.Subsequently,a lightweight hierarchical MLP communication module is designed to implement hierarchical feature interactions in both horizontal and vertical dimensions,through which cross-modal deep semantic fusion is achieved.It is demonstrated by the experimental results that compared with the suboptimal model on CMU-MOSI,The Acc2 and F1 values increase 0.89 percentage points and 0.77 percentage points compared with the suboptimal model.Furthermore,all metrics in comparative experiments on CMU-MOSEI are shown to surpass those of baseline models,with the Acc2 and F1 values being elevated to 86.34%and 86.25%.