Moving Object Segmentation: All You Need is SAM (and Flow)

Junyu Xie, Charig Yang, Weidi Xie, Andrew Zisserman

Abstract: The objective of this paper is motion segmentation – discovering and segmenting the moving objects in a video. This is a much studied area with numerous careful, and sometimes complex, approaches and training schemes including: self-supervised learning, learning from synthetic datasets, object-centric representations, amodal representations, and many more. Our interest in this paper is to determine if the Segment Anything model (SAM) can contribute to this task.