  We present a model-agnostic post-processing scheme to improve the boundary
quality for the segmentation result that is generated by any existing
segmentation model. Motivated by the empirical observation that the label
predictions of interior pixels are more reliable, we propose to replace the
originally unreliable predictions of boundary pixels by the predictions of
interior pixels. Our approach processes only the input image through two steps:
(i) localize the boundary pixels and (ii) identify the corresponding interior
pixel for each boundary pixel. We build the correspondence by learning a
direction away from the boundary pixel to an interior pixel. Our method
requires no prior information of the segmentation models and achieves nearly
real-time speed. We empirically verify that our SegFix consistently reduces the
boundary errors for segmentation results generated from various
state-of-the-art models on Cityscapes, ADE20K and GTA5. Code is available at:
https://github.com/openseg-group/openseg.pytorch.
