{"url":"/method/sabl","slug":"sabl","name":"SABL","full_name":"Side-Aware Boundary Localization","full_name_withheld":false,"description_markdown":"**Side-Aware Boundary Localization (SABL)** is a methodology for precise localization in object detection where each side of the bounding box is respectively localized with a dedicated network branch. Empirically, the authors observe that when they manually annotate a bounding box for an object, it is often much easier to align each side of the box to the object boundary than to move the\r\nbox as a whole while tuning the size. Inspired by this observation, in SABL each side of the bounding box is respectively positioned based on its surrounding context. \r\n\r\nAs shown in the Figure, the authors devise a bucketing scheme to improve the localization precision. For each side of a bounding box, this scheme divides the target space into multiple buckets, then determines the bounding box via two steps. Specifically, it first searches for the correct bucket, i.e., the one in which the boundary resides. Leveraging the centerline of the selected buckets as a\r\ncoarse estimate, fine regression is then performed by predicting the offsets. This scheme allows very precise localization even in the presence of displacements with large variance. Moreover, to preserve precisely localized bounding boxes in the non-maximal suppression procedure, the authors also propose to adjust the classification score based on the bucketing confidences, which leads to further performance gains.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Side-Aware Boundary Localization for More Precise Object Detection","paper":"/paper/side-aware-boundary-localization-for-more","first_author":"Jiaqi Wang","n_authors":9,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/side-aware-boundary-localization-for-more"},"source":{"url":"https://arxiv.org/abs/1912.04260v2","title":"Side-Aware Boundary Localization for More Precise Object Detection","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":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models","date":"2021-11-14","arxiv_id":"2111.07355","n_code_links":0,"syntology":null},{"paper":"/paper/side-aware-boundary-localization-for-more","title":"Side-Aware Boundary Localization for More Precise Object Detection","date":"2019-12-09","arxiv_id":"1912.04260","n_code_links":3,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/object-detection","name":"Object Detection","papers":2},{"task":"/task/object-detection-1","name":"object-detection","papers":2},{"task":"/task/ensemble-learning","name":"Ensemble Learning","papers":1},{"task":"/task/fracture-detection","name":"Fracture detection","papers":1},{"task":"/task/medical-object-detection","name":"Medical Object Detection","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1},{"task":"/task/regression-1","name":"regression","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2019","papers":1},{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/sabl"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}