Zhe Chen2,1† Jiannan Wu3,1†, Wenhai Wang1,4, Weijie Su6,1†, Guo Chen2,1†, Sen Xing5, Muyan Zhong5, Qinglong Zhang1, Xizhou Zhu5,7,1, Lewei Lu7,1, Bin Li6, Ping Luo3, Tong Lu2,
Yu Qiao1, Jifeng Dai5
1B1OpenGVLab, Shanghai AI Laboratory 2Nanjing University
3The University of Hong Kong 4The Chinese University of Hong Kong 5Tsinghua University
6University of Science and Technology of China 7SenseTime Research
The exponential growth of large language models (LLMs) has opened up numerous possibilities for multimodal 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 LLM, using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition, visionlanguage tasks such as zero-shot image/video classification, zero-shot image/video-text retrieval, and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models.