  In this report, we present the transferring pretrained video mask
autoencoders(VideoMAE) to egocentric tasks for Ego4d Looking at me Challenge.
VideoMAE is the data-efficient pretraining model for self-supervised video
pre-training and can easily transfer to downstream tasks. We show that the
representation transferred from VideoMAE has good Spatio-temporal modeling and
the ability to capture small actions. We only need to use egocentric data to
train 10 epochs based on VideoMAE which pretrained by the ordinary videos
acquired from a third person's view, and we can get better results than the
baseline on Ego4d Looking at me Challenge.
