Summary of Measuring Cross-modal Interactions in Multimodal Models, by Laura Wenderoth et al.
Measuring Cross-Modal Interactions in Multimodal Models
by Laura Wenderoth, Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach to explainable AI (XAI) in healthcare is introduced, addressing the limitations of existing methods that focus on unimodal models and fail to capture cross-modal interactions. The proposed method, InterSHAP, uses the Shapley interaction index to precisely quantify the contributions of individual modalities and their interactions without approximations. This enables accurate measurement of cross-modal interactions in multimodal settings, providing detailed explanations at a local level for individual samples. The open-source implementation of InterSHAP is integrated with the SHAP package, enhancing reproducibility and ease of use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) can greatly help healthcare by improving patient care and system efficiency. However, AI systems often lack explainability, making it hard to understand how they work. This paper introduces a new way to make AI more understandable called InterSHAP. It works by analyzing how different types of data interact with each other. This helps doctors and researchers understand how AI makes decisions and provides personalized explanations for individual patients. |
