| Summary: | Animals have evolved highly functional visual systems to
understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. We make the
following contributions: First, we introduce a simple variant of the Transformer to segment optical flow frames into
primary objects and the background. Second, we train the
architecture in a self-supervised manner, i.e. without using
any manual annotations. Third, we analyze several critical
components of our method and conduct thorough ablation
studies to validate their necessity. Fourth, we evaluate the
proposed architecture on public benchmarks (DAVIS2016,
SegTrackv2, and FBMS59). Despite using only optical flow
as input, our approach achieves superior or comparable results to previous state-of-the-art self-supervised methods,
while being an order of magnitude faster. We additionally evaluate on a challenging camouflage dataset (MoCA),
significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised
approach, highlighting the importance of motion cues, and
the potential bias towards visual appearance in existing
video segmentation models.
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