mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 13040-13051

Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However previous methods have primarily focused on enhancing multi-modal capabilities. In this work we introduce a versatile multi-modal large language model mPLUG-Owl2 which effectively leverages modality collaboration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design with the language decoder acting as a universal interface for managing different modalities. Specifically mPLUG-Owl2 incorporates shared functional modules to facilitate modality collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experiments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks while achieving state-of-the-art performances with a single generalized model. Notably mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios setting a pioneering path in the development of future multi-modal foundation models.