  Video semantic segmentation is active in recent years benefited from the
great progress of image semantic segmentation. For such a task, the per-frame
image segmentation is generally unacceptable in practice due to high
computation cost. To tackle this issue, many works use the flow-based feature
propagation to reuse the features of previous frames. However, the optical flow
estimation inevitably suffers inaccuracy and then causes the propagated
features distorted. In this paper, we propose distortion-aware feature
correction to alleviate the issue, which improves video segmentation
performance by correcting distorted propagated features. To be specific, we
firstly propose to transfer distortion patterns from feature into image space
and conduct effective distortion map prediction. Benefited from the guidance of
distortion maps, we proposed Feature Correction Module (FCM) to rectify
propagated features in the distorted areas. Our proposed method can
significantly boost the accuracy of video semantic segmentation at a low price.
The extensive experimental results on Cityscapes and CamVid show that our
method outperforms the recent state-of-the-art methods.
