Boundary Attention: Learning to Find Faint Boundaries at Any Resolution
Authors: name: Mia Gaia Polansky, name: Charles Herrmann, name: Junhwa Hur, name: Deqing Sun, name: Dor Verbin, name: Todd Zickler

We present a differentiable model that explicitly models boundaries --
including contours, corners and junctions -- using a new mechanism that we call
boundary attention. We show that our model provides accurate results even when
the boundary signal is very weak or is swamped by noise. Compared to previous
classical methods for finding faint boundaries, our model has the advantages of
being differentiable; being scalable to larger images; and automatically
adapting to an appropriate level of geometric detail in each part of an image.
Compared to previous deep methods for finding boundaries via end-to-end
training, it has the advantages of providing sub-pixel precision, being more
resilient to noise, and being able to process any image at its native
resolution and aspect ratio.