Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

2026-05-22

AI Summary

This study addresses the lack of systematic investigation into the role of Mixture-of-Experts (MoE) in multimodal learning. It presents the first integrative analytical framework bridging MoE and multimodal learning, examining its applications through three complementary lenses: as an efficient computational engine, a representation learner, and an adapter. The work systematically reviews how MoE enhances computational scalability, cross-modal alignment, and modeling under imperfect data conditions. Drawing on a comprehensive literature survey, it synthesizes key technical aspects—including routing mechanisms, expert selection, representation alignment, and handling of missing modalities—into a unified theoretical framework. The paper further identifies critical research gaps, such as interpretable routing, inter-expert communication, adaptive modality fusion, and continual learning, thereby charting a path toward building interpretable and sustainable multimodal MoE systems.

Abstract

Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Despite its growing success, a comprehensive and systematic review on the MoE metho addressing multimodal challenges remains lacking. Existing surveys tend to evaluate either multimodal learning or MoE independently from method taxonomy, overlooking the unique interplay between them. This survey fills that gap by answering a central question: \textit{How does MoE effectively resolve multimodal challenges?} We approach this from three key perspectives: (1) \textbf{MoE as an Efficient Multimodal Engine:} enabling scalable multimodal modeling by decoupling computational cost from parameter growth and mitigating modality redundancy through selective expert activation; (2) \textbf{MoE as a Multimodal Representation Learner:} integrating complementary multi-opinion expert knowledge to enrich alignment and interaction representations; and (3) \textbf{MoE as a Multimodal Adapter:} providing a modular and flexible mechanism to model imperfect data scenarios such as modality imbalance and missing modality. Through our extensive literature review, we identify critical research gaps, including interpretable routing, expert communication, modality integration, and lifelong multimodal learning. We position this survey as a foundation for future research toward interpretable and sustainable multimodal Mixture-of-Experts system.

Problem

• Mixture-of-Experts
• multimodal learning
• modality integration
• missing modality
• modality imbalance

Innovation

• Mixture-of-Experts
• multimodal learning
• selective expert activation
• modality alignment
• missing modality adaptation