  Several recent works have directly extended the image masked autoencoder
(MAE) with random masking into video domain, achieving promising results.
However, unlike images, both spatial and temporal information are important for
video understanding. This suggests that the random masking strategy that is
inherited from the image MAE is less effective for video MAE. This motivates
the design of a novel masking algorithm that can more efficiently make use of
video saliency. Specifically, we propose a motion-guided masking algorithm
(MGM) which leverages motion vectors to guide the position of each mask over
time. Crucially, these motion-based correspondences can be directly obtained
from information stored in the compressed format of the video, which makes our
method efficient and scalable. On two challenging large-scale video benchmarks
(Kinetics-400 and Something-Something V2), we equip video MAE with our MGM and
achieve up to +$1.3\%$ improvement compared to previous state-of-the-art
methods. Additionally, our MGM achieves equivalent performance to previous
video MAE using up to $66\%$ fewer training epochs. Lastly, we show that MGM
generalizes better to downstream transfer learning and domain adaptation tasks
on the UCF101, HMDB51, and Diving48 datasets, achieving up to +$4.9\%$
improvement compared to baseline methods.
