Mixpert: Mitigating Multimodal Learning Conflicts with Efficient Mixture-of-Vision-Experts

2025-05-30

AI Summary

Single visual encoders in multimodal large language models (MLLMs) suffer from domain conflicts under multi-task learning, limiting task-specific representation capability. Method: We propose an efficient Visual Mixture-of-Experts (V-MoE) architecture that employs a lightweight dynamic gating mechanism to adaptively route input images to specialized visual experts, enabling fine-grained, task-aware feature extraction. Crucially, V-MoE preserves end-to-end joint optimization—unlike multi-encoder approaches—while supporting modular reconfiguration of visual encoders and seamless integration with arbitrary MLLMs. Contribution/Results: Extensive experiments demonstrate substantial improvements in multi-task performance across diverse vision-language benchmarks. V-MoE achieves superior accuracy with significantly lower computational overhead compared to multi-encoder baselines, offering a scalable and generalizable paradigm for enhancing visual representation capacity in MLLMs.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Multimodal large language models (MLLMs) require a nuanced interpretation of complex image information, typically leveraging a vision encoder to perceive various visual scenarios. However, relying solely on a single vision encoder to handle diverse task domains proves difficult and inevitably leads to conflicts. Recent work enhances data perception by directly integrating multiple domain-specific vision encoders, yet this structure adds complexity and limits the potential for joint optimization. In this paper, we introduce Mixpert, an efficient mixture-of-vision-experts architecture that inherits the joint learning advantages from a single vision encoder while being restructured into a multi-expert paradigm for task-specific fine-tuning across different visual tasks. Additionally, we design a dynamic routing mechanism that allocates input images to the most suitable visual expert. Mixpert effectively alleviates domain conflicts encountered by a single vision encoder in multi-task learning with minimal additional computational cost, making it more efficient than multiple encoders. Furthermore, Mixpert integrates seamlessly into any MLLM, with experimental results demonstrating substantial performance gains across various tasks.

Problem

• Addressing conflicts in multimodal learning with diverse vision tasks
• Reducing complexity of multiple vision encoders in MLLMs
• Enhancing performance across visual tasks with dynamic expert routing

Innovation

• Efficient mixture-of-vision-experts architecture
• Dynamic routing for optimal expert allocation
• Seamless integration into multimodal language models