  This paper proposes a new framework for semantic segmentation of objects in
videos. We address the label inconsistency problem of deep convolutional neural
networks (DCNNs) by exploiting the fact that videos have multiple frames; in a
few frames the object is confidently-estimated (CE) and we use the information
in them to improve labels of the other frames. Given the semantic segmentation
results of each frame obtained from DCNN, we sample several CE frames to adapt
the DCNN model to the input video by focusing on specific instances in the
video rather than general objects in various circumstances. We propose offline
and online approaches under different supervision levels. In experiments our
method achieved great improvement over the original model and previous
state-of-the-art methods.
