  Unsupervised video object segmentation (VOS) aims to detect the most salient
object in a video sequence at the pixel level. In unsupervised VOS, most
state-of-the-art methods leverage motion cues obtained from optical flow maps
in addition to appearance cues to exploit the property that salient objects
usually have distinctive movements compared to the background. However, as they
are overly dependent on motion cues, which may be unreliable in some cases,
they cannot achieve stable prediction. To reduce this motion dependency of
existing two-stream VOS methods, we propose a novel motion-as-option network
that optionally utilizes motion cues. Additionally, to fully exploit the
property of the proposed network that motion is not always required, we
introduce a collaborative network learning strategy. On all the public
benchmark datasets, our proposed network affords state-of-the-art performance
with real-time inference speed.
