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InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Open accessAbstract
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, andADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.
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de Oliveira, L. Y. D., Paes, J. L. A. R., de Melo, M. J., Oliveira, M. D. R., Bezerra, E. T., Ramos, A. P. M., Li, J., Damasceno‐Júnior, G. A., Gonçalves, W. N., & Marcato, J. (2025) · Caminhos de Geografia · doi:10.14393/rcg2610776023
Rotation-Robust Deep Watermarking for Arbitrary Angular Transformations
Kong, X. (2025) · International Conference on Intelligent Perception and Pattern Recognition (IPPR) · doi:10.1109/ippr66507.2025.11198354
Enriched Pedestrian Crossing Prediction Using Carla Synthetic Data
Azarmi, M., Rezaei, M., & Wang, H. (2025) · IET Intelligent Transport Systems · doi:10.1049/itr2.70104
MemRoadNet: Human-like Memory Integration for Free Road Space Detection
Shafiq, S., Khan, A. A., & Shao, J. (2025) · Sensors · doi:10.3390/s25216600
MMPX: Multi-modal Mamba Prompter to Large Vision Foundation Model for RGB-X Semantic Segmentation
Wu, P., Liu, Y., Gao, H., & Liu, J. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209345
Metadata
- Authors
- Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., Wang, X., & Qiao, Y.
- Affiliations
- Shanghai Artificial Intelligence Laboratory (CN)
- Sense Time Research
- Nanjing University
- Chinese University of Hong Kong (HK)
- ORCID
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 14408-14419
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076222
- Cited by
- 1,080
- References
- 85
- Citations by year
- 2026: 197 · 2025: 382 · 2024: 362 · 2023: 138
- FWCI
- 115.39
- 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, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China (61672273,61832008)
- Topics
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- deformable convolution, receptive fields, inductive bias, object detection, semantic segmentation, image classification
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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