  The task of semi-supervised video object segmentation (VOS) has been greatly
advanced and state-of-the-art performance has been made by dense matching-based
methods. The recent methods leverage space-time memory (STM) networks and learn
to retrieve relevant information from all available sources, where the past
frames with object masks form an external memory and the current frame as the
query is segmented using the mask information in the memory. However, when
forming the memory and performing matching, these methods only exploit the
appearance information while ignoring the motion information. In this paper, we
advocate the return of the \emph{motion information} and propose a motion
uncertainty-aware framework (MUNet) for semi-supervised VOS. First, we propose
an implicit method to learn the spatial correspondences between neighboring
frames, building upon a correlation cost volume. To handle the challenging
cases of occlusion and textureless regions during constructing dense
correspondences, we incorporate the uncertainty in dense matching and achieve
motion uncertainty-aware feature representation. Second, we introduce a
motion-aware spatial attention module to effectively fuse the motion feature
with the semantic feature. Comprehensive experiments on challenging benchmarks
show that \textbf{\textit{using a small amount of data and combining it with
powerful motion information can bring a significant performance boost}}. We
achieve ${76.5\%}$ $\mathcal{J} \& \mathcal{F}$ only using DAVIS17 for
training, which significantly outperforms the \textit{SOTA} methods under the
low-data protocol. \textit{The code will be released.}
