ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

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. 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.

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• 1 For example, modern ConvNets, represented by ConvNeXt , have demonstrated strong performance in various scenarios.
• 2 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) .
• 3 However, we found that simply combining these two approaches leads to subpar performance.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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