  The rise of social media and the exponential growth of multimodal
communication necessitates advanced techniques for Multimodal Information
Extraction (MIE). However, existing methodologies primarily rely on direct
Image-Text interactions, a paradigm that often faces significant challenges due
to semantic and modality gaps between images and text. In this paper, we
introduce a new paradigm of Image-Context-Text interaction, where large
multimodal models (LMMs) are utilized to generate descriptive textual context
to bridge these gaps. In line with this paradigm, we propose a novel Shapley
Value-based Contrastive Alignment (Shap-CA) method, which aligns both
context-text and context-image pairs. Shap-CA initially applies the Shapley
value concept from cooperative game theory to assess the individual
contribution of each element in the set of contexts, texts and images towards
total semantic and modality overlaps. Following this quantitative evaluation, a
contrastive learning strategy is employed to enhance the interactive
contribution within context-text/image pairs, while minimizing the influence
across these pairs. Furthermore, we design an adaptive fusion module for
selective cross-modal fusion. Extensive experiments across four MIE datasets
demonstrate that our method significantly outperforms existing state-of-the-art
methods.
