all AI news
Boundary Attention: Learning to Localize Boundaries under High Noise
March 20, 2024, 4:46 a.m. | Mia Gaia Polansky, Charles Herrmann, Junhwa Hur, Deqing Sun, Dor Verbin, Todd Zickler
cs.CV updates on arXiv.org arxiv.org
Abstract: We present a differentiable model that infers explicit boundaries, including curves, corners and junctions, using a mechanism that we call boundary attention. Boundary attention is a boundary-aware local attention operation that, when applied densely and repeatedly, progressively refines a field of variables that specify an unrasterized description of the local boundary structure in every overlapping patch within an image. It operates in a bottom-up fashion, similar to classical methods for sub-pixel edge localization and edge-linking, …
abstract arxiv attention call cs.cv differentiable local attention noise type variables
More from arxiv.org / cs.CV updates on arXiv.org
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
2 days, 1 hour ago |
arxiv.org
Fingerprint Matching with Localized Deep Representation
2 days, 1 hour ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne