  We explore the efficiency of the CRF inference beyond image level semantic
segmentation and perform joint inference in video frames. The key idea is to
combine best of two worlds: semantic co-labeling and more expressive models.
Our formulation enables us to perform inference over ten thousand images within
seconds and makes the system amenable to perform video semantic segmentation
most effectively. On CamVid dataset, with TextonBoost unaries, our proposed
method achieves up to 8% improvement in accuracy over individual semantic image
segmentation without additional time overhead. The source code is available at
https://github.com/subtri/video_inference
