Information-Theoretic Decomposition for Multimodal Interaction Learning
Zequn Yang, Yake Wei, Haotian Ni, Zhihao Xu, Di Hu
Why It Matters
What makes this one worth your time
Understanding and leveraging sample-specific interactions can significantly enhance the performance of multimodal systems, which are increasingly relevant in AI applications.
DMIL offers a new approach to adaptively learn sample-specific interactions in multimodal learning.
Summary
The paper introduces a novel paradigm called Decomposition-based Multimodal Interaction Learning (DMIL) that focuses on learning sample-specific interactions in multimodal learning using an information-theoretic approach. It proposes a variational decomposition architecture to isolate interaction components and a new learning strategy to leverage these components for improved performance across diverse tasks.
Key contributions
- Introduction of DMIL, a new paradigm for multimodal interaction learning.
- Development of a variational decomposition architecture for isolating interaction components.
- A novel learning strategy that fine-tunes based on explicit interaction components.
Notable insights
- The use of a variational decomposition architecture to isolate interaction components is a clever methodology.
- The focus on sample-specific interactions addresses a critical gap in current multimodal learning paradigms.
Possible limitations
- Not stated in the abstract
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 multimodal learning. Our analysis further reveals deficits in conventional paradigms at learning these distinct interaction types: modality ensemble approaches struggle to capture synergy, while joint learning paradigms often under-utilize redundant information. This highlights the need for an approach that can adaptively learn from different interaction types on a per-sample basis. To this end, we propose Decomposition-based Multimodal Interaction Learning (DMIL), a novel paradigm that explicitly models and learns from sample-specific interactions. First, we design a variational decomposition architecture to isolate the constituent interaction components. Second, we employ a new learning strategy that leverages these explicit interaction components in a fine-tuning process to achieve comprehensive interaction learning. Extensive experiments across diverse tasks and architectures demonstrate that DMIL consistently achieves superior performance by adapting to holistic sample-specific interactions. Our framework is flexible and broadly applicable, establishing an interaction-centric paradigm for multimodal learning. The code is available at https://github.com/GeWu-Lab/DMIL.