title: Boundary Attention: Learning to Find Faint Boundaries at Any Resolution
summary: 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.
paper_authors._id: 6594ccf589f1ff04632ec971
paper_authors.name: Mia Gaia Polansky
paper_authors._id: 6594ccf589f1ff04632ec972
paper_authors.name: Charles Herrmann
paper_authors._id: 6594ccf589f1ff04632ec973
paper_authors.name: Junhwa Hur
paper_authors._id: 6594ccf589f1ff04632ec974
paper_authors.name: Deqing Sun
paper_authors._id: 6594ccf589f1ff04632ec975
paper_authors.name: Dor Verbin
paper_authors._id: 6594ccf589f1ff04632ec976
paper_authors.name: Todd Zickler
paper_title: Boundary Attention: Learning to Find Faint Boundaries at Any Resolution
paper_summary: 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.
paper_ai_summary: A differentiable model using boundary attention accurately detects weak and noisy boundaries in images, offering scalable, adaptive, and sub-pixel precise performance.
paper_ai_keywords: differentiable model boundary attention contours corners junctions sub-pixel precision end-to-end training