InternVL: Scaling up Vision Foundation Models and Aligning for Generic
  Visual-Linguistic Tasks
The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However, the progress in vision and
vision-language foundation models, which are also critical elements of
multi-modal AGI, has not kept pace with LLMs. In this work, we design a
large-scale vision-language foundation model (InternVL), which scales up the
vision foundation model to 6 billion parameters and progressively aligns it
with the large language model, using web-scale image-text data from various
sources. This model can be broadly applied to and achieve state-of-the-art
performance on visual perception tasks such as image-level or pixel-level
recognition, vision-language tasks such as zero-shot image/video
classification, zero-shot image/video-text retrieval, and link with LLMs to
create multi-modal dialogue systems. We hope that our research could contribute
to the development of multi-modal large models. Code and models are available
at https://github.com/OpenGVLab/InternVL.
658a48652021ba68d7d0e606
Zhe Chen
claimed_verified
2024-12-09T14:51:29.868Z
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Zhe Chen
user
czczup
658a48652021ba68d7d0e607
Jiannan Wu
claimed_verified
2025-08-08T16:25:38.635Z
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/avatars/149c6d813956e228567811770df01859.svg
Jiannan Wu
user
wjn922
658a48652021ba68d7d0e608
Wenhai Wang
658a48652021ba68d7d0e609
Weijie Su
claimed_verified
2025-06-19T09:12:33.836Z
63e4562f9db5da2dc1f3b520
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Weijie Su
user
jackroos
658a48652021ba68d7d0e60a
Guo Chen
claimed_verified
2024-03-19T08:53:39.601Z
6392c73390b8e99a6779a7b0
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Guo Chen
user
cg1177
658a48652021ba68d7d0e60b
Sen Xing
658a48652021ba68d7d0e60c
Zhong Muyan
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Qinglong Zhang
658a48652021ba68d7d0e60e
Xizhou Zhu
658a48652021ba68d7d0e60f
Lewei Lu
658a48652021ba68d7d0e610
Bin Li
658a48652021ba68d7d0e611
Ping Luo
658a48652021ba68d7d0e612
Tong Lu
658a48652021ba68d7d0e613
Yu Qiao
658a48652021ba68d7d0e614
Jifeng Dai
InternVL: Scaling up Vision Foundation Models and Aligning for Generic
  Visual-Linguistic Tasks
The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However, the progress in vision and
vision-language foundation models, which are also critical elements of
multi-modal AGI, has not kept pace with LLMs. In this work, we design a
large-scale vision-language foundation model (InternVL), which scales up the
vision foundation model to 6 billion parameters and progressively aligns it
with the large language model, using web-scale image-text data from various
sources. This model can be broadly applied to and achieve state-of-the-art
performance on visual perception tasks such as image-level or pixel-level
recognition, vision-language tasks such as zero-shot image/video
classification, zero-shot image/video-text retrieval, and link with LLMs to
create multi-modal dialogue systems. We hope that our research could contribute
to the development of multi-modal large models. Code and models are available
at https://github.com/OpenGVLab/InternVL.
A large-scale vision-language foundation model with 6 billion parameters achieves state-of-the-art performance on various visual and vision-language tasks and can integrate with LLMs to develop multi-modal dialogue systems.
vision-language foundation model InternVL vision foundation model web-scale image-text data image-level recognition pixel-level recognition zero-shot image/video classification zero-shot image/video-text retrieval multi-modal dialogue systems