Cited by 1,080Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningVisual Attention and Saliency Detection

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InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions

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Wang, Wenhai, Dai, Jifeng, Chen, Zhe, Huang, Zhenhang, Li, Zhiqi, Zhu, Xizhou, Hu, Xiaowei, Lu, Tong, Lu, Lewei, Li, Hongsheng, Wang, Xiaogang, Qiao, Yu·2023·2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr52729.2023.01385

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Abstract

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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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: Wang, Wenhai
Dai, Jifeng
Chen, Zhe
Huang, Zhenhang
Li, Zhiqi
Hu, Xiaowei
Lu, Lewei
Li, Hongsheng
Wang, Xiaogang
Qiao, Yu
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)
DOI: 10.1109/cvpr52729.2023.01385
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
Licence: cc-by
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, 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

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