  Recently, several Space-Time Memory based networks have shown that the object
cues (e.g. video frames as well as the segmented object masks) from the past
frames are useful for segmenting objects in the current frame. However, these
methods exploit the information from the memory by global-to-global matching
between the current and past frames, which lead to mismatching to similar
objects and high computational complexity. To address these problems, we
propose a novel local-to-local matching solution for semi-supervised VOS,
namely Regional Memory Network (RMNet). In RMNet, the precise regional memory
is constructed by memorizing local regions where the target objects appear in
the past frames. For the current query frame, the query regions are tracked and
predicted based on the optical flow estimated from the previous frame. The
proposed local-to-local matching effectively alleviates the ambiguity of
similar objects in both memory and query frames, which allows the information
to be passed from the regional memory to the query region efficiently and
effectively. Experimental results indicate that the proposed RMNet performs
favorably against state-of-the-art methods on the DAVIS and YouTube-VOS
datasets.
