Self-supervised learning (SSL) techniques have recently produced outstanding results in learning visual representations from unlabeled videos. However, despite the importance of motion in supervised learning techniques for action recognition, SSL methods often do not explicitly consider motion information in videos. To address this issue, we propose MOFO (MOtion FOcused), a novel SSL method for focusing representation learning on the motion area of a video for action recognition. MOFO automatically detects motion areas in videos and uses these to guide the self-supervision task. We use a masked autoencoder that randomly masks out a high proportion of the input sequence and forces a specified percentage of the inside of the motion area to be masked and the remainder from outside. We further incorporate motion information into the finetuning step to emphasise motion in the downstream task. We demonstrate that our motion-focused innovations can significantly boost the performance of the currently leading SSL method (VideoMAE) for action recognition. Our proposed approach significantly improves the performance of the current SSL method for action recognition, indicating the importance of explicitly encoding motion in SSL.
@inproceedings{Ahmadian:NeurIPS:2023,
AUTHOR = Ahmadian, Mona and Guerin, Frank and Gilbert, Andrew",
TITLE = "MOFO: MOtion FOcused Self-Supervision for Video Understanding",
BOOKTITLE = " NeurIPS 2023 Workshop Self-Supervised Learning: Theory and Practice",
YEAR = "2023",
}