Masked autoencoding has shown excellent performance on self-supervised video
representation learning. Temporal redundancy has led to a high masking ratio
and customized masking strategy in VideoMAE. In this paper, we aim to further
improve the performance of video masked autoencoding by introducing a motion
guided masking strategy. Our key insight is that motion is a general and unique
prior in video, which should be taken into account during masked pre-training.
Our motion guided masking explicitly incorporates motion information to build
temporal consistent masking volume. Based on this masking volume, we can track
the unmasked tokens in time and sample a set of temporal consistent cubes from
videos. These temporal aligned unmasked tokens will further relieve the
information leakage issue in time and encourage the MGMAE to learn more useful
structure information. We implement our MGMAE with an online efficient optical
flow estimator and backward masking map warping strategy. We perform
experiments on the datasets of Something-Something V2 and Kinetics-400,
demonstrating the superior performance of our MGMAE to the original VideoMAE.
In addition, we provide the visualization analysis to illustrate that our MGMAE
can sample temporal consistent cubes in a motion-adaptive manner for more
effective video pre-training.

本文介绍了一种新的视频自编码方法，采用运动引导的掩模策略，通过引入运动信息建立时间一致的掩模体积，从而提高视频自编码的性能。通过实验证明，该方法在视频预训练中能够更有效地处理时间一致性和信息泄漏问题。