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Masked autoencoders are scalable vision learners, as the title of MAE\cite{he2022masked}, which suggests that self-supervised learning (SSL) invision might undertake a similar trajectory as in NLP. Specifically, generativepretext tasks with the masked prediction (e.g., BERT) have become a de factostandard SSL practice in NLP. By contrast, early attempts at generative methodsin vision have been buried by their discriminative counterparts (likecontrastive learning); however, the success of mask image modeling has revivedthe masking autoencoder (often termed denoising autoencoder in the past). As amilestone to bridge the gap with BERT in NLP, masked autoencoder has attractedunprecedented attention for SSL in vision and beyond. This work conducts acomprehensive survey of masked autoencoders to shed insight on a promisingdirection of SSL. As the first to review SSL with masked autoencoders, thiswork focuses on its application in vision by discussing its historicaldevelopments, recent progress, and implications for diverse applications.

A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond