Methods › Computer Vision › Layout Annotation Models › BoundaryNet
BoundaryNet
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
BoundaryNet is a resizing-free approach for layout annotation. The variable-sized user selected region of interest is first processed by an attention-guided skip network. The network optimization is guided via Fast Marching distance maps to obtain a good quality initial boundary estimate and an associated feature representation. These outputs are processed by a Residual Graph Convolution Network optimized using Hausdorff loss to obtain the final region boundary.
Papers archive 2025-07-28
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BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation 21 Aug 2021 · 1 repository · arXiv:2108.09433
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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