InternVL: Scaling up Vision Foundation Models and Aligning for Generic
  Visual-Linguistic Tasks
Authors: name: Zhe Chen, name: Jiannan Wu, name: Wenhai Wang, name: Weijie Su, name: Guo Chen, name: Sen Xing, name: Zhong Muyan, name: Qinglong Zhang, name: Xizhou Zhu, name: Lewei Lu, name: Bin Li, name: Ping Luo, name: Tong Lu, name: Yu Qiao, name: Jifeng Dai

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.