  This paper studies a conceptually simple extension of Masked Autoencoders
(MAE) to spatiotemporal representation learning from videos. We randomly mask
out spacetime patches in videos and learn an autoencoder to reconstruct them in
pixels. Interestingly, we show that our MAE method can learn strong
representations with almost no inductive bias on spacetime (only except for
patch and positional embeddings), and spacetime-agnostic random masking
performs the best. We observe that the optimal masking ratio is as high as 90%
(vs. 75% on images), supporting the hypothesis that this ratio is related to
information redundancy of the data. A high masking ratio leads to a large
speedup, e.g., > 4x in wall-clock time or even more. We report competitive
results on several challenging video datasets using vanilla Vision
Transformers. We observe that MAE can outperform supervised pre-training by
large margins. We further report encouraging results of training on real-world,
uncurated Instagram data. Our study suggests that the general framework of
masked autoencoding (BERT, MAE, etc.) can be a unified methodology for
representation learning with minimal domain knowledge.
