Summary of Shapley Value-based Contrastive Alignment For Multimodal Information Extraction, by Wen Luo and Yu Xia and Shen Tianshu and Sujian Li
Shapley Value-based Contrastive Alignment for Multimodal Information Extraction
by Wen Luo, Yu Xia, Shen Tianshu, Sujian Li
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a new paradigm for Multimodal Information Extraction (MIE) by utilizing large multimodal models (LMMs) to generate descriptive textual context, bridging semantic and modality gaps between images and text. The proposed Shapley Value-based Contrastive Alignment (Shap-CA) method aligns context-text and context-image pairs, initially evaluating the individual contribution of each element using cooperative game theory’s Shapley value concept. A contrastive learning strategy is employed to enhance interactive contributions while minimizing influence across pairs. An adaptive fusion module is designed for selective cross-modal fusion. The method significantly outperforms existing state-of-the-art methods on four MIE datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to extract information from social media and other mixed-text-and-image sources. Currently, most methods just compare images with text directly, but this doesn’t always work well because they might be very different in meaning or style. The new approach uses big models that can generate helpful context for both the text and the image. This makes it easier to find connections between them. The researchers tested their method on four different datasets and found that it works better than other methods. |
Keywords
» Artificial intelligence » Alignment
