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 · 2025
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.