{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/simultaneous-edge-alignment-and-learning","title":"Simultaneous Edge Alignment and Learning","arxiv_id":"1808.01992","date":"2018-08-06","proceeding":"ECCV 2018 9","authors":["Zhiding Yu","Weiyang Liu","Yang Zou","Chen Feng","Srikumar Ramalingam","B. V. K. Vijaya Kumar","Jan Kautz"],"abstract":"Edge detection is among the most fundamental vision problems for its role in\nperceptual grouping and its wide applications. Recent advances in\nrepresentation learning have led to considerable improvements in this area.\nMany state of the art edge detection models are learned with fully\nconvolutional networks (FCNs). However, FCN-based edge learning tends to be\nvulnerable to misaligned labels due to the delicate structure of edges. While\nsuch problem was considered in evaluation benchmarks, similar issue has not\nbeen explicitly addressed in general edge learning. In this paper, we show that\nlabel misalignment can cause considerably degraded edge learning quality, and\naddress this issue by proposing a simultaneous edge alignment and learning\nframework. To this end, we formulate a probabilistic model where edge alignment\nis treated as latent variable optimization, and is learned end-to-end during\nnetwork training. Experiments show several applications of this work, including\nimproved edge detection with state of the art performance, and automatic\nrefinement of noisy annotations.","url_abs":"http://arxiv.org/abs/1808.01992v3","url_pdf":"http://arxiv.org/pdf/1808.01992v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"simultaneous-edge-alignment-and-learning","repo_url":"https://github.com/Chrisding/seal","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"simultaneous-edge-alignment-and-learning","repo_url":"https://github.com/Chrisding/sbd-preprocess","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"simultaneous-edge-alignment-and-learning","repo_url":"https://github.com/zhusiling/SEAL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"edge-detection","task_name":"Edge Detection"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.01992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.01992"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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