Cited by 843Multimodal Machine Learning ApplicationsAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

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Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

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Chen, Zhe, Wu, Jiannan, Wang, Wenhai, Su, Weijie, Chen, Guo, Xing, Sen, Zhong, Muyan, Zhang, Qinglong, Zhu, Xizhou, Lu, Lewei, Li, Bin, Luo, Ping, Lu, Tong, Qiao, Yu, Dai, Jifeng·2024·2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr52733.2024.02283

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Abstract

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 foun-dation model (Intern VL), which scales up the vision foun-dation 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, 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. 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.

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Authors: Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., Li, B., Luo, P., Lu, T., Qiao, Y., & Dai, J.
Affiliations: Nanjing University (CN)
University of Hong Kong (HK)
Chinese University of Hong Kong (HK)
University of Science and Technology of China (CN)
Tsinghua University (CN)
Shanghai Artificial Intelligence Laboratory (CN)
Sensetime (China) (CN)
Nanjing University
Tsinghua University
ORCID: Chen, Zhe
Wu, Jiannan
Wang, Wenhai
Chen, Guo
Zhang, Qinglong
Lu, Lewei
Li, Bin
Venue: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2024-06-16
Pages: 24185-24198
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr52733.2024.02283
OpenAlex: W4402713111
Cited by: 734
References: 145
Citations by year: 2026: 360 · 2025: 331 · 2024: 43
FWCI: 151.51
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Natural Science Foundation of China (62376134,62372223)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: vision-language foundation models, vision foundation models, LLM alignment, zero-shot image classification, image-text retrieval, visual perception

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Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

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