Explore the Limits of Omni-modal Pretraining at Scale
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
666bada43637dcab935c1fce
Yiyuan Zhang
admin_assigned
2024-06-14T11:42:10.249Z
63176933b58b0184630d2c74
https://cdn-avatars.huggingface.co/v1/production/uploads/63176933b58b0184630d2c74/53b5EASwW76zeyyqeJA3O.jpeg
Yiyuan Zhang
user
Yiyuan
666bada43637dcab935c1fcf
Handong Li
666bada43637dcab935c1fd0
Jing Liu
666bada43637dcab935c1fd1
Xiangyu Yue
Explore the Limits of Omni-modal Pretraining at Scale
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
Multimodal Context (MiCo) is a scalable pretraining paradigm that enhances multimodal intelligence across various tasks and modalities, achieving state-of-the-art performance.
Multimodal Context MiCo multimodal intelligence scalable pretraining single-modality perception cross-modality understanding multimodal large language model