OneLLM: One Framework to Align All Modalities with Language
Multimodal large language models (MLLMs) have gained significant attention
due to their strong multimodal understanding capability. However, existing
works rely heavily on modality-specific encoders, which usually differ in
architecture and are limited to common modalities. In this paper, we present
OneLLM, an MLLM that aligns eight modalities to language using a unified
framework. We achieve this through a unified multimodal encoder and a
progressive multimodal alignment pipeline. In detail, we first train an image
projection module to connect a vision encoder with LLM. Then, we build a
universal projection module (UPM) by mixing multiple image projection modules
and dynamic routing. Finally, we progressively align more modalities to LLM
with the UPM. To fully leverage the potential of OneLLM in following
instructions, we also curated a comprehensive multimodal instruction dataset,
including 2M items from image, audio, video, point cloud, depth/normal map, IMU
and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks,
encompassing tasks such as multimodal captioning, question answering and
reasoning, where it delivers excellent performance. Code, data, model and
online demo are available at https://github.com/csuhan/OneLLM
65712870c8018fe6408b5376
Jiaming Han
claimed_verified
2023-12-07T08:29:55.390Z
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Jiaming Han
user
csuhan
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Kaixiong Gong
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2023-12-07T11:38:07.316Z
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Kaixiong Gong
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kxgong
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Yiyuan Zhang
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2024-01-29T08:28:41.137Z
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Yiyuan Zhang
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Yiyuan
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Jiaqi Wang
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2023-12-07T11:39:41.220Z
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Jiaqi Wang
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Kaipeng Zhang
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2023-12-07T11:39:49.235Z
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KAIPENG ZHANG
user
kpzhang
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Dahua Lin
admin_assigned
2023-12-07T11:39:55.752Z
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Dahua Lin
user
lindahua
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Yu Qiao
65712870c8018fe6408b537d
Peng Gao
65712870c8018fe6408b537e
Xiangyu Yue
OneLLM: One Framework to Align All Modalities with Language
Multimodal large language models (MLLMs) have gained significant attention
due to their strong multimodal understanding capability. However, existing
works rely heavily on modality-specific encoders, which usually differ in
architecture and are limited to common modalities. In this paper, we present
OneLLM, an MLLM that aligns eight modalities to language using a unified
framework. We achieve this through a unified multimodal encoder and a
progressive multimodal alignment pipeline. In detail, we first train an image
projection module to connect a vision encoder with LLM. Then, we build a
universal projection module (UPM) by mixing multiple image projection modules
and dynamic routing. Finally, we progressively align more modalities to LLM
with the UPM. To fully leverage the potential of OneLLM in following
instructions, we also curated a comprehensive multimodal instruction dataset,
including 2M items from image, audio, video, point cloud, depth/normal map, IMU
and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks,
encompassing tasks such as multimodal captioning, question answering and
reasoning, where it delivers excellent performance. Code, data, model and
online demo are available at https://github.com/csuhan/OneLLM
A unified framework aligns multiple modalities to language using OneLLM, a multimodal large language model, demonstrating strong performance across various tasks.
multimodal large language models OneLLM unified multimodal encoder progressive multimodal alignment pipeline image projection module universal projection module dynamic routing multimodal instruction dataset multimodal captioning question answering reasoning