@inproceedings{
luo2024shapley,
title={Shapley Value-based Contrastive Alignment for  Multimodal Information Extraction},
author={Wen Luo and Yu Xia and Shen Tianshu and Sujian Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=QOmkk07hIH}
}
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 face the 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. Code will be released upon acceptance.
Shapley Value-based Contrastive Alignment for  Multimodal Information Extraction
Wen Luo Yu Xia Shen Tianshu Sujian Li