MetaMorph: Unified Visual Understanding and Generation by LeCun

Explore MetaMorph, a multimodal AI model merging visual understanding and generation. Discover insights from LeCun, Xie, Liu, and others.

Today, multimodal large language models (MLLMs) have made significant progress in the field of visual understanding, with visual instruction tuning methods being widely adopted.

This approach is advantageous in terms of data and computational efficiency, and its effectiveness demonstrates that large language models (LLMs) possess substantial inherent visual knowledge, enabling them to effectively learn and develop visual understanding during instruction tuning.

In a paper co-authored by researchers from Meta and New York University, the authors explore whether LLMs can also generate visual information with similar efficiency and effectiveness through fine-tuning.

The paper's authors include several renowned AI scholars, such as Turing Award winner Yann LeCun, NYU Assistant Professor of Computer Science Saining Xie, and FAIR Research Scientist Zhuang Liu (who will join Princeton University as an Assistant Professor in the Department of Computer Science next September).