Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
Liangwei Nathan Zheng, Wei Emma Zhang, Olaf Maennel, Lin Yue, Weitong Chen
Why It Matters
What makes this one worth your time
This survey provides a structured overview of MoE's potential in multimodal contexts, highlighting critical areas for future research that could improve model efficiency and effectiveness.
A survey exploring how Mixture-of-Experts can enhance multimodal learning.
Summary
The paper presents a survey on the Mixture-of-Experts (MoE) framework in the context of multimodal learning, addressing its adaptability and efficiency while identifying research gaps in the field.
Key contributions
- Systematic review of the MoE framework applied to multimodal learning.
- Identification of critical research gaps such as interpretable routing and modality integration.
- Proposing MoE as a flexible mechanism for addressing imperfect data scenarios.
Notable insights
- MoE can decouple computational cost from parameter growth, which is crucial for scaling multimodal models.
- The integration of multi-opinion expert knowledge could lead to better representation learning in multimodal scenarios.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.27431v1 Announce Type: cross 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.