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arXiv · 2412.11694

From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities

Shixin Jiang·Jiafeng Liang·Jiyuan Wang·Xuan Dong·Heng Chang·Weijiang Yu·Jinhua Du·Ming Liu·Bing Qin
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Abstract

To tackle complex tasks in real-world scenarios, more researchers are focusing on Omni-MLLMs, which aim to achieve omni-modal understanding and generation. Beyond the constraints of any specific non-linguistic modality, Omni-MLLMs map various non-linguistic modalities into the embedding space of LLMs and enable the interaction and understanding of arbitrary combinations of modalities within a single model. In this paper, we systematically investigate relevant research and provide a comprehensive survey of Omni-MLLMs. Specifically, we first explain the four core components of Omni-MLLMs for unified multi-modal modeling with a meticulous taxonomy that offers novel perspectives. Then, we introduce the effective integration achieved through two-stage training and discuss the corresponding datasets as well as evaluation. Furthermore, we summarize the main challenges of current Omni-MLLMs and outline future directions. We hope this paper serves as an introduction for beginners and promotes the advancement of related research. Resources have been made publicly available at https://github.com/threegold116/Awesome-Omni-MLLMs.

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Shixin Jiang, Jiafeng Liang, Jiyuan Wang, Xuan Dong, Heng Chang, Weijiang Yu, Jinhua Du, Ming Liu, Bing Qin. 2024-12-16. From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities. https://arxiv.org/abs/2412.11694

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