  We explore the efficiency of the CRF inference module beyond image level
semantic segmentation. The key idea is to combine the best of two worlds of
semantic co-labeling and exploiting more expressive models. Similar to
[Alvarez14] our formulation enables us perform inference over ten thousand
images within seconds. On the other hand, it can handle higher-order clique
potentials similar to [vineet2014] in terms of region-level label consistency
and context in terms of co-occurrences. We follow the mean-field updates for
higher order potentials similar to [vineet2014] and extend the spatial
smoothness and appearance kernels [DenseCRF13] to address video data inspired
by [Alvarez14]; thus making the system amenable to perform video semantic
segmentation most effectively.
