ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
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Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt [33], have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE) [14]. However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.
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Masked autoencoding can be combined with convolutional encoders, while requiring attention to the interaction between masking and convolutional architectures.
“Related developments in computer vision, such as ConvNeXt V2 and VideoMAC, also show that masked autoencoding can be combined with convolutional encoders, while requiring attention to the interaction between masking and convolutional architectures43,44.”
From Siamese masked reconstruction with temporal rotation consistency for inertial-based human activity recognition (2026) · cites this paper as [43]
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- Authors
- Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I. S., & Xie, S.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Meta AI, FAIR
- New York University (US)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 16133-16142
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076493
- Cited by
- 1,791
- References
- 46
- Citations by year
- 2026: 577 · 2025: 692 · 2024: 418 · 2023: 104
- FWCI
- 191.28
- 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-nc-nd)
- arXiv (Cornell University)
- 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 › Advanced Image and Video Retrieval Techniques
- Keywords
- ConvNeXt V2, masked autoencoder, self-supervised learning, convolutional neural networks, image classification, object detection, semantic segmentation, pre-trained models, model scaling
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