  We present TDNet, a temporally distributed network designed for fast and
accurate video semantic segmentation. We observe that features extracted from a
certain high-level layer of a deep CNN can be approximated by composing
features extracted from several shallower sub-networks. Leveraging the inherent
temporal continuity in videos, we distribute these sub-networks over sequential
frames. Therefore, at each time step, we only need to perform a lightweight
computation to extract a sub-features group from a single sub-network. The full
features used for segmentation are then recomposed by application of a novel
attention propagation module that compensates for geometry deformation between
frames. A grouped knowledge distillation loss is also introduced to further
improve the representation power at both full and sub-feature levels.
Experiments on Cityscapes, CamVid, and NYUD-v2 demonstrate that our method
achieves state-of-the-art accuracy with significantly faster speed and lower
latency.
