What is recorded here?
Introduced an asymmetric masked-autoencoder design that reconstructs randomly masked image patches from a high masking ratio and reported scalable self-supervised pretraining results on ImageNet benchmarks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact six-author CVPR 2022 paper. It does not absorb language masked modeling, all image autoencoders, later MAE variants or performance claims outside the reported models, data and evaluations.
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People and systems
Kaiming He; Xinlei Chen; Saining Xie; Yanghao Li; Piotr Dollár; Ross Girshick
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
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English catalogue narrative
He and colleagues: masked autoencoders for scalable vision learning
Introduced an asymmetric masked-autoencoder design that reconstructs randomly masked image patches from a high masking ratio and reported scalable self-supervised pretraining results on ImageNet benchmarks.
Scientific scope
The record is the exact six-author CVPR 2022 paper. It does not absorb language masked modeling, all image autoencoders, later MAE variants or performance claims outside the reported models, data and evaluations.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Kaiming He; Xinlei Chen; Saining Xie; Yanghao Li; Piotr Dollár; Ross Girshick
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.