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Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
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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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“Context-aware modeling is widely used across various domains, including natural language processing [219], computer vision [220], and recommendation systems [221], where it can substantially boost both system performance and user experience.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [219]
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Zhang, S., Sun, P., Chen, S., Xiao, M., Shao, W., Zhang, W., Liu, Y., Chen, K., & Luo, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91813-1_4 · Cited by 323
Self-citationEVA-CLIP: Improved Training Techniques for CLIP at ScalePreprint
Sun, Q., Fang, Y., Wu, L., Wang, X., & Cao, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.15389 · Cited by 299
(2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 291
A Diagram is Worth a Dozen Images
Kembhavi, A., Salvato, M., Kolve, E., Seo, M., Hajishirzi, H., & Farhadi, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_15 · Cited by 290
TextCaps: A Dataset for Image Captioning with Reading Comprehension
Sidorov, O., Hu, R., Rohrbach, M., & Singh, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_44 · Cited by 283
Monkey: Image Resolution and Text Label are Important Things for Large Multi-Modal Models
Li, Z., Yang, B., Liu, Q., Ma, Z., Zhang, S., Yang, J., Sun, Y., Liu, Y., & Bai, X. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02527 · Cited by 283
OCR-VQA: Visual Question Answering by Reading Text in Images
Mishra, A., Shekhar, S., Singh, A. K., & Chakraborty, A. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00156 · Cited by 281
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingPreprint
Zhang, Y., Zhang, R., Gu, J., Zhou, Y., Lipka, N., Yang, D., & Sun, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.17107 · Cited by 278
ViperGPT: Visual Inference via Python Execution for Reasoning
Surís, D., Menon, S., & Vondrick, C. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01092 · Cited by 273
Emu: Generative Pretraining in MultimodalityPreprint
Sun, Q., Yu, Q., Cui, Y., Zhang, F., Zhang, X., Wang, Y., Gao, H., Liu, J., Huang, T., & Wang, X. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.05222 · Cited by 261
Chinese CLIP: Contrastive Vision-Language Pretraining in ChinesePreprint
An, Y., Pan, J., Lin, J., Men, R., Zhang, Y., Zhou, J., & Zhou, C. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.01335 · Cited by 237
PaLI: A Jointly-Scaled Multilingual Language-Image ModelPreprint
Chen, X., Wang, X., Changpinyo, S., Piergiovanni, A., Padlewski, P., Salz, D., Goodman, S., Grycner, A., Mustafa, B., Beyer, L., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.06794 · Cited by 235
ResT: An Efficient Transformer for Visual RecognitionPreprint
Zhang, Q., & Yang, Y.-B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.13677 · Cited by 214
Self-citationVisual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
Wu, C., Yin, S., Qi, W., Wang, X., Tang, Z., & Duan, N. (2023) · arXiv · doi:10.48550/arxiv.2303.04671 · Cited by 197
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningPreprint
Lu, P., Qiu, L., Chen, J., Xia, T., Zhao, Y., Zhang, W., Zhou, Y., Liang, X., & Zhu, S. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.13214 · Cited by 177
Beyer, L., Hénaff, O. J., Kolesnikov, A., Zhai, X., & Oord, A. V. D. (2020) · arXiv · doi:10.48550/arxiv.2006.07159 · Cited by 176
Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., & Launay, J. (2023) · arXiv · doi:10.48550/arxiv.2306.01116 · Cited by 164
SVIT: Scaling up Visual Instruction TuningPreprint
Zhao, B., Wu, B., He, M., & Huang, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.04087 · Cited by 157
ONE-PEACE: Exploring One General Representation Model Toward Unlimited ModalitiesPreprint
Wang, P., Wang, S., Lin, J., Bai, S., Zhou, X., Zhou, J., Wang, X., & Zhou, C. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.11172 · Cited by 156
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Xu, L., Yang, J., Zheng, X., Li, K., Sun, X. W., et al. (2025) · neural information processing systems · doi:10.52202/085713-4899 · Cited by 148
NExT-GPT: Any-to-Any Multimodal LLMPreprint
Wu, S., Fei, H., Qu, L., Ji, W., & Chua, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.05519 · Cited by 132
Kosmos-2: Grounding Multimodal Large Language Models to the WorldPreprint
Peng, Z., Wang, W., Dong, L., Hao, Y., Huang, S., Ma, S., & Wei, F. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.14824 · Cited by 129
Self-citationInternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and GenerationPreprint
Wang, Y., He, Y., Li, Y., Li, K., Yu, J., Ma, X., Li, X., Guo, C., Chen, X., Wang, Y., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.06942 · Cited by 126
Self-citationBaichuan 2: Open Large-scale Language Models
Yang, A., Xiao, B., Wang, B., Zhang, B., Bian, C., Yin, C., Lv, C., Pan, D., Wang, D., Yan, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.10305 · Cited by 125
Self-citationSkywork: A More Open Bilingual Foundation ModelPreprint
Wei, T., Zhao, L., Zhang, L., Zhu, B., Wang, L., Yang, H., Li, B., Cheng, C., Lü, W., Hu, R., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.19341 · Cited by 125
Self-citationZhang, P., Dong, X., Wang, B., Cao, Y., Xu, C., Ouyang, L., Zhao, Z., Duan, H., Zhang, S., Ding, S., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.15112 · Cited by 124
Self-citationScaling vision transformers to 22 billion parameters
Dehghani, M., Djolonga, J., Mustafa, B., Padlewski, P., Heek, J., Gilmer, J., Steiner, A., Caron, M., Geirhos, R., Alabdulmohsin, I., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.05442 · Cited by 112
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond LanguagePreprint
Liu, Z., He, Y., Wang, W., Wang, W., Wang, Y., Chen, S., Zhang, Q., Lai, Z., Yang, Y., Li, Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.05662 · Cited by 112
Self-citationAltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
Chen, Z., Liu, G., Zhang, B., Yang, Q., & Wu, L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.552 · Cited by 109
Self-citationFluency-Guided Cross-Lingual Image Captioning
Lan, W., Li, X., & Dong, J. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123366 · Cited by 95
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Wang, X., Liu, Y., Shen, C., Ng, C. C., Luo, C., Jin, L., Chan, C. S., van den Hengel, A., & Wang, L. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01014 · Cited by 90
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive IntelligencePreprint
Zhang, J., Gan, R., Wang, J., Zhang, Y., Zhang, L., Yang, P., Gao, X., Wu, Z., Dong, X., He, J., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.02970 · Cited by 89
Self-citationVideoChat: Chat-Centric Video UnderstandingPreprint
Li, K., He, Y., Wang, Y., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.06355 · Cited by 89
Otter: A Multi-Modal Model with In-Context Instruction TuningPreprint
Li, B., Zhang, Y., Chen, L., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.03726 · Cited by 86
Self-citationWukong: A 100 Million Large-Scale Chinese Cross-Modal Pre-Training Benchmark
Gu, J., Meng, X., Lu, G., Hou, L., Minzhe, N., Liang, X., Yao, L., Huang, R., Zhang, W., Jiang, X., et al. (2022) · neural information processing systems · doi:10.52202/068431-1916 · Cited by 78
The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open WorldPreprint
Wang, W., Shi, M., Li, Q., Wang, W., Huang, Z., Xing, L., Chen, Z., Li, H., Zhu, X., Cao, Z., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.01907 · Cited by 75
Self-citationmPLUG-DocOwl: Modularized Multimodal Large Language Model for Document UnderstandingPreprint
Ye, J., Hu, A., Xu, H., Ye, Q., Yan, M., Dan, Y., Zhao, C., Xu, G., Li, C., Tian, J., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.02499 · Cited by 74
The effectiveness of MAE pre-pretraining for billion-scale pretraining
Singh, M., Duval, Q., Alwala, K. V., Fan, H., Aggarwal, V., Adcock, A., Joulin, A., Dollár, P., Feichtenhofer, C., Girshick, R., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00505 · Cited by 70
ControlLLM: Augment Language Models with Tools by Searching on Graphs
Liu, Z., Lai, Z., Gao, Z., Cui, E., Li, Z., Zhu, X., Lu, L., Chen, Q., Qiao, Y., Dai, Y., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73254-6_6 · Cited by 60
Self-citationDreamLLM: Synergistic Multimodal Comprehension and CreationPreprint
Dong, R., Han, C., Peng, Y., Qi, Z., Ge, Z., Yang, J., Zhao, L., Sun, J., Zhou, H., Wei, H., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.11499 · Cited by 59
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of ThoughtPreprint
Mu, Y., Zhang, Q., Hu, M., Wang, W., Ding, M., Jin, J., Wang, B., Dai, J., Qiao, Y., & Luo, P. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.15021 · Cited by 58
Self-citationCai, Y., Zhou, Y., Han, Q., Sun, J., Kong, X., Li, J., & Zhang, X. (2022) · arXiv · doi:10.48550/arxiv.2212.11696 · Cited by 56
Contrastive Language-Image Pre-training for the Italian LanguagePreprint
Bianchi, F., Attanasio, G., Pisoni, R., Terragni, S., Sarti, G., & Lakshmi, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.08688 · Cited by 43
PaLI-X: On Scaling up a Multilingual Vision and Language ModelPreprint
Chen, X., Djolonga, J., Padlewski, P., Mustafa, B., Changpinyo, S., Wu, J., Riquelme, C., Goodman, S., Wang, X., Tay, Y., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.18565 · Cited by 42
Self-citationThe Physiological Response to the Feeling of Being WatchedPreprint
Carere, M. T. (2018) · EasyChair preprint · doi:10.29007/h68j · Cited by 31
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkPreprint
Gu, J., Meng, X., Lu, G., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.06767 · Cited by 29
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
Yang, R., Song, L., Li, Y., Zhao, S., Ge, Y., Li, X., & Shan, Y. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-3149 · Cited by 25
An Empirical Study of Scaling Instruct-Tuned Large Multimodal ModelsPreprint
Lu, Y., Li, C., Liu, H., Yang, J., Gao, J., & Shen, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.09958 · Cited by 21
ResT V2: Simpler, Faster and Stronger
Zhang, Q., & Yang, Y.-B. (2022) · neural information processing systems · doi:10.52202/068431-2640 · Cited by 17
Self-citationAlleviating data insufficiency for Chinese sign language recognition
Xue, W., Liu, J., Yan, S., Zhou, Y., Yuan, T., & Guo, Q. (2023) · Visual Intelligence · doi:10.1007/s44267-023-00028-5 · Cited by 15
ResT V2: Simpler, Faster and StrongerPreprint
Zhang, Q., & Yang, Y.-B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.07366 · Cited by 12
Self-citationToloka Visual Question Answering BenchmarkPreprint
Ustalov, D., Pavlichenko, N., Koshelev, S., Likhobaba, D., & Smirnova, A. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.16511 · Cited by 1
Flagged first, then by citations
7 of the 145 references couldn’t be matched to a known paper.
Research works printed but not linked: 23
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Cited by
Sorted by citations
How far are we to GPT-4V? Closing the gap to commercial multimodal models with open-source suites
Chen, Z., Wang, W., Tian, H., Ye, S., Gao, Z., Cui, E., Tong, W., Hu, K., Luo, J., Ma, Z., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4231-5 · Cited by 1,058
Self-citationMMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Yue, X., Ni, Y., Zheng, T., Zhang, K., Liu, R., Zhang, G., Stevens, S., Jiang, D., Ren, W., Sun, Y., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00913 · Cited by 429
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349
InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
Wang, Y., Li, K., Li, X., Yu, J., He, Y., Chen, G., Pei, B., Zheng, R., Wang, Z., Shi, Y., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73013-9_23 · Cited by 427
Self-citationLlaVA-CoT: Let Vision Language Models Reason Step-By-Step
Xu, G., Jin, P., Wu, Z., Li, H., Song, Y., Sun, L., & Yuan, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00202 · Cited by 118
Metadata
- 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
- 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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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