ConvNeXtV2: fully convolutional masked autoencoding and global response normalization

What changed

Co-designed a convolutional masked-autoencoding framework with Global Response Normalization to enhance inter-channel feature competition.

Scientific scope

Co-designed a convolutional masked-autoencoding framework with Global Response Normalization to enhance inter-channel feature competition. Inherits ConvNeXt and masked autoencoders. Delta is the FCMAE/GRN mechanism and its empirical integration, not merely a larger ConvNeXt release or proof of universally better representations.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits ConvNeXt and masked autoencoders. Delta is the FCMAE/GRN mechanism and its empirical integration, not merely a larger ConvNeXt release or proof of universally better representations.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

Full scientific record · Browse contribution grades · Request a correction