Information-Theoretic Decomposition for Multimodal Interaction Learning

📰 ArXiv cs.AI

Learn to decompose multimodal interactions using information theory to improve learning outcomes

advanced Published 11 Jun 2026

Action Steps

Apply information-theoretic decomposition to identify redundant, unique, and synergistic information across modalities
Use this decomposition to learn sample-specific interactions
Configure multimodal learning models to incorporate these dynamic interactions
Test the performance of the models on multimodal datasets
Compare the results with traditional multimodal learning approaches

Who Needs to Know This

Researchers and engineers working on multimodal learning tasks can benefit from this approach to better understand and capture dynamic interactions across modalities

Key Insight

💡 Dynamic, sample-specific interactions across modalities are critical for effective multimodal learning

Key Takeaways

Learn to decompose multimodal interactions using information theory to improve learning outcomes

Full Article

Title: Information-Theoretic Decomposition for Multimodal Interaction Learning

Abstract:
arXiv:2606.11614v1 Announce Type: cross Abstract: Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal interactions. A critical yet underexplored challenge is that these implicit interactions vary dynamically across samples. In this work, we present the first systematic, information-theoretic analysis highlighting why learning these dynamic, sample-specific interactions is critical for effective multimo

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