title: InternVL: Scaling up Vision Foundation Models and Aligning for Generic
    Visual-Linguistic Tasks
summary: 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.
paper_authors._id: 658a48652021ba68d7d0e606
paper_authors.name: Zhe Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-09T14:51:29.868Z
paper_authors._id: 64119264f0f81eb569e0d569
paper_authors.avatarUrl: /avatars/00dccedea51ac7d2f398ca9d65a8e78b.svg
paper_authors.fullname: Zhe Chen
paper_authors.type: user
paper_authors.user: czczup
paper_authors._id: 658a48652021ba68d7d0e607
paper_authors.name: Jiannan Wu
paper_authors._id: 658a48652021ba68d7d0e608
paper_authors.name: Wenhai Wang
paper_authors._id: 658a48652021ba68d7d0e609
paper_authors.name: Weijie Su
paper_authors._id: 658a48652021ba68d7d0e60a
paper_authors.name: Guo Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-19T08:53:39.601Z
paper_authors._id: 6392c73390b8e99a6779a7b0
paper_authors.avatarUrl: /avatars/9ff824ab02848120aec5e8de6780bcf1.svg
paper_authors.fullname: Guo Chen
paper_authors.type: user
paper_authors.user: cg1177
paper_authors._id: 658a48652021ba68d7d0e60b
paper_authors.name: Sen Xing
paper_authors._id: 658a48652021ba68d7d0e60c
paper_authors.name: Zhong Muyan
paper_authors._id: 658a48652021ba68d7d0e60d
paper_authors.name: Qinglong Zhang
paper_authors._id: 658a48652021ba68d7d0e60e
paper_authors.name: Xizhou Zhu
paper_authors._id: 658a48652021ba68d7d0e60f
paper_authors.name: Lewei Lu
paper_authors._id: 658a48652021ba68d7d0e610
paper_authors.name: Bin Li
paper_authors._id: 658a48652021ba68d7d0e611
paper_authors.name: Ping Luo
paper_authors._id: 658a48652021ba68d7d0e612
paper_authors.name: Tong Lu
paper_authors._id: 658a48652021ba68d7d0e613
paper_authors.name: Yu Qiao
paper_authors._id: 658a48652021ba68d7d0e614
paper_authors.name: Jifeng Dai
paper_title: InternVL: Scaling up Vision Foundation Models and Aligning for Generic
    Visual-Linguistic Tasks
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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