Explore the Limits of Omni-modal Pretraining at Scale
Authors: name: Yiyuan Zhang, name: Handong Li, name: Jing Liu, name: Xiangyu Yue

We propose to build omni-modal intelligence, which is capable of
understanding any modality and learning universal representations. In specific,
we propose a scalable pretraining paradigm, named Multimodal Context (MiCo),
which can scale up the numbers of modalities and amount of data, together with
the model parameters, in the pretraining process. With MiCo, the pretrained
models show significant emergent abilities in multimodal learning, which are
evaluated on the following tasks: i) single-modality perception benchmarks of
10 different modalities, ii) 25 cross-modality understanding tasks of
retrieval, question-answering, captioning, and iii) 18 multimodal large
language model benchmarks. Our models establish 37 new records for
state-of-the-art performance. We hope that our research could contribute to the
development of omni-modal intelligence. Code and Models are at
https://github.com/invictus717/MiCo