  Video segmentation approaches are of great importance for numerous vision
tasks especially in video manipulation for entertainment. Due to the challenges
associated with acquiring high-quality per-frame segmentation annotations and
large video datasets with different environments at scale, learning approaches
shows overall higher accuracy on test dataset but lack strict temporal
constraints to self-correct jittering artifacts in most practical applications.
We investigate how this jittering artifact degrades the visual quality of video
segmentation results and proposed a metric of temporal stability to numerically
evaluate it. In particular, we propose a Coherent Loss with a generic framework
to enhance the performance of a neural network against jittering artifacts,
which combines with high accuracy and high consistency. Equipped with our
method, existing video object/semantic segmentation approaches achieve a
significant improvement in term of more satisfactory visual quality on video
human dataset, which we provide for further research in this field, and also on
DAVIS and Cityscape.
