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

🤖 Plain-English Summary

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.

🔑 Key Findings

• For example, modern ConvNets, represented by ConvNeXt , 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) .
• However, we found that simply combining these two approaches leads to subpar performance.

🔗 Related Resources