MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models

TL;DR

This paper introduces MoIIE, a novel mixture of intra- and inter-modality experts for large vision-language models, improving efficiency and performance in multi-modal tasks by jointly modeling modality-specific and cross-modal features.

Contribution

The paper proposes a new MoE architecture with intra- and inter-modality experts and a two-stage training strategy, enhancing multi-modal learning in LVLMs.

Findings

MoIIE achieves comparable or better performance than larger models.

The approach improves parameter efficiency in multi-modal tasks.

Extensive experiments validate the effectiveness and generality of MoIIE.

Abstract

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across multi-modal tasks by scaling model size and training data. However, these dense LVLMs incur significant computational costs and motivate the exploration of sparse Mixture of Experts (MoE) architectures. While MoE improve parameter efficiency, effectively applying MoE to simultaneously model modality-specific features and cross-modal associations in LVLMs remains challenging. In this work, we propose to incorporate Mixture of Intra- and Inter-Modality Experts (MoIIE) to LVLMs. For each token, expert routing is guided by its modality, directing tokens to their respective intra-modality experts as well as a shared pool of inter-modality experts, enabling the model to jointly learn rich intra-modal features and cross-modal interactions. We further introduce an effective and straightforward two-stage training strategy, which facilitates the direct activation of both MoE and multi-modal capabilities. Extensive experiments across different data scales and LLM backbone demonstrate the effectiveness, efficiency and generality of our approach. Notably, our MoIIE models with 5.5B and 11.3B activated parameters match or even surpass the performance of existing advanced open-source MoE-LLMs based multi-modal models that involve more activated parameters. The code is available at https://github.com/AlenjandroWang/MoIIE.