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
64119264f0f81eb569e0d569
/avatars/00dccedea51ac7d2f398ca9d65a8e78b.svg
Zhe Chen
user
czczup
658a48652021ba68d7d0e607
Jiannan Wu
658a48652021ba68d7d0e608
Wenhai Wang
658a48652021ba68d7d0e609
Weijie Su
claimed_verified
2025-06-19T09:12:33.836Z
63e4562f9db5da2dc1f3b520
/avatars/f4eecf1396b05e1c72436e7026d85cef.svg
Weijie Su
user
jackroos
658a48652021ba68d7d0e60a
Guo Chen
claimed_verified
2024-03-19T08:53:39.601Z
6392c73390b8e99a6779a7b0
/avatars/9ff824ab02848120aec5e8de6780bcf1.svg
Guo Chen
user
cg1177
658a48652021ba68d7d0e60b
Sen Xing
658a48652021ba68d7d0e60c
Zhong Muyan
658a48652021ba68d7d0e60d
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