| Challenge: | unified multimodal large language models (MLLMs) are emerging but lack a systematic framework to connect them and situate current trends within a broader landscape. |
| Approach: | They present a systematic review of unified Multimodal Large Language Models . they outline the foundational concepts and prerequisites for understanding them . |
| Outcome: | The present review provides a systematic and systematic overview of unified MLLMs . it discusses persistent challenges and identify promising directions for future research . |
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Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang, Jiayao Ma, Kai Zhou, Junfeng Luo
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| Challenge: | MultiModal Large Language Models (MM-LLMs) have undergone significant advances in the past year . traditional MM models incur substantial computational costs, especially when trained from scratch . |
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| Challenge: | MLLMs have achieved significant breakthroughs in understanding across text and vision, but current models still face inconsistencies in reasoning outcomes. |
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| Challenge: | This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs. |
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| Challenge: | Existing studies have focused on the ability of MLLMs to generate single tokens one by one, while lacking studies about how their representation vectors can encode global multimodal information. |
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From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities (2025.findings-acl)
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Shixin Jiang, Jiafeng Liang, Jiyuan Wang, Xuan Dong, Heng Chang, Weijiang Yu, Jinhua Du, Ming Liu, Bing Qin
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