Mixpert: Mitigating Multimodal Learning Conflicts with Efficient Mixture-of-Vision-Experts
This addresses domain conflicts in multimodal large language models for improved visual task handling, though it is incremental as it builds on existing mixture-of-experts and routing mechanisms.
The paper tackles the problem of multimodal learning conflicts in MLLMs by proposing Mixpert, an efficient mixture-of-vision-experts architecture, which results in substantial performance gains across various tasks with minimal computational cost.
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.