Multi-level global and local fusion for multimodal sentiment analysis
TL;DR
DualScope is proposed, a novel model that combines a global-local fusion strategy with bidirectional image-text generation for semantically consistent data augmentation and introduces both label contrastive learning and data contrastive learning to align heterogeneous modalities and enhance model robustness.
Abstract
With the rise of social media, multimodal sentiment analysis has gained increasing attention due to the rich and diverse ways users express emotions through text and images. However, existing methods often ignore fine-grained sentiment cues and semantic misalignment across modalities, limiting this task effectiveness. To address these challenges, we propose DualScope, a novel model that combines a global-local fusion strategy with bidirectional image-text generation for semantically consistent data augmentation. Furthermore, we introduce both label contrastive learning and data contrastive learning to align heterogeneous modalities and enhance model robustness. The fusion module integrates global and local features in a progressive manner under global semantic guidance. Extensive experiments on two widely used datasets, MVSA-Single and MVSA-Multiple, demonstrate that the proposed method achieves superior performance, confirming its effectiveness in handling fine-grained semantics and cross-modal inconsistency.