{"url":"/method/boundarynet","slug":"boundarynet","name":"BoundaryNet","full_name":"BoundaryNet","full_name_withheld":false,"description_markdown":"**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](https://paperswithcode.com/method/convolution) Network optimized using Hausdorff loss to obtain the final region boundary.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2108.09433v1","title":"BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Layout Annotation Models","url":"/methods/category/layout-annotation-models","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/boundarynet-an-attentive-deep-network-with","title":"BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation","date":"2021-08-21","arxiv_id":"2108.09433","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[],"tasks_shown":0,"n_tasks":0,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/boundarynet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}