  This paper proposes a novel active boundary loss for semantic segmentation.
It can progressively encourage the alignment between predicted boundaries and
ground-truth boundaries during end-to-end training, which is not explicitly
enforced in commonly used cross-entropy loss. Based on the predicted boundaries
detected from the segmentation results using current network parameters, we
formulate the boundary alignment problem as a differentiable direction vector
prediction problem to guide the movement of predicted boundaries in each
iteration. Our loss is model-agnostic and can be plugged into the training of
segmentation networks to improve the boundary details. Experimental results
show that training with the active boundary loss can effectively improve the
boundary F-score and mean Intersection-over-Union on challenging image and
video object segmentation datasets.
