  In this paper, we propose a novel approach to learning semantic contextual
relationships in videos for semantic object segmentation. Our algorithm derives
the semantic contexts from video object proposals which encode the key
evolution of objects and the relationship among objects over the
spatio-temporal domain. This semantic contexts are propagated across the video
to estimate the pairwise contexts between all pairs of local superpixels which
are integrated into a conditional random field in the form of pairwise
potentials and infers the per-superpixel semantic labels. The experiments
demonstrate that our contexts learning and propagation model effectively
improves the robustness of resolving visual ambiguities in semantic video
object segmentation compared with the state-of-the-art methods.
