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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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465

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Research works printed but not linked: 23

Achiam, 2023, Gpt-4 technical report, arXiv preprint
Bai, 2023, Qwen tech-nical report, arXiv preprint
Bai, 2023, Qwen-vl: A frontier large vision-language model with versatile abilities, arXiv preprint
Byeon, 2022, Coyo-700m: Image-text pair dataset
Carlsson, 2022, Cross-lingual and multilingual clip, LREC, 6848
Chen, 2023, Shikra: Unleashing multi-modal 11ms referential dialogue magic, arXiv preprint
Dosovitskiy, 2020, An image is worth 16×16 words: Transformers for image recognition at scale, ICLR
Goyal, 2017, Making the v in vq a matter: El-evating the role of image understanding in visual question answering, CVPR, 6904
2023, Introducing idefics: An open reproduction of state-of-the-art visual language model
Ilharco, 2021, Open-clip. Zenodo. Version 0.1
2023, Clip benchmark: Clip-like model evaluation
Li, 2023, Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 19730
Li, 2019, Coco-en for cross-lingual image tagging, captioning, and retrieval, TMM, 21, 2347
Liu, 2023, Aligning large multi-modal model with robust instruction tuning, arXiv preprint
Sawada, 2022, Construction and public release of language image pretraining models in japanese, MIRU
Oquab, 2023, Dinov2: Learning robust visual features with-out supervision, TMLR
Schuhmann, 2022, Laion coco:600m synthetic captions from laion2b-en
Sun, 2024, Generative pretraining in mul-timodality, ICLR
Sun, 2023, Moss: Training conversationallanguage models from synthetic data, arXiv preprint, 7
Taori, 2023, Alpaca: A strong, replicable instruction-following model, Stanford Center for Research on Foundation Models, 3, 7
Team, 2023, Internlm: A multilingual language model with progressively enhanced capabilities
Xie, 2022, Zero and r2d2: A large-scale chi-nese cross-modal benchmark and a vision-language frame-work, arXiv preprint
Zhu, 2023, Ghost in the minecraft: Generally capable agents for open-world enviroments via large language models with text-based knowledge and memory, arXiv preprint

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Li, M., Ye, D., Zhou, Y., & Yang, C. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246478
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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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