{"url":"/method/extremenet","slug":"extremenet","name":"ExtremeNet","full_name":"ExtremeNet","full_name_withheld":false,"description_markdown":"**ExtremeNet** is a a bottom-up object detection framework that detects four extreme points (top-most, left-most, bottom-most, right-most) of an object. It uses a keypoint estimation framework to find extreme points, by predicting four multi-peak heatmaps for each object category. In addition, it uses one [heatmap](https://paperswithcode.com/method/heatmap) per category predicting the object center, as the average of two bounding box edges in both the x and y dimension. We group extreme points into objects with a purely geometry-based approach. We group four extreme points, one from each map, if and only if their\r\ngeometric center is predicted in the center heatmap with a score higher than a pre-defined threshold, We enumerate all $O\\left(n^{4}\\right)$ combinations of extreme point prediction, and select the valid ones.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Bottom-up Object Detection by Grouping Extreme and Center Points","paper":"/paper/bottom-up-object-detection-by-grouping","first_author":"Xingyi Zhou","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/bottom-up-object-detection-by-grouping"},"source":{"url":"http://arxiv.org/abs/1901.08043v3","title":"Bottom-up Object Detection by Grouping Extreme and Center Points","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/xingyizhou/ExtremeNet","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"One-Stage Object Detection Models","url":"/methods/category/one-stage-object-detection-models","pwc_aliases":[]},{"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":"/paper/houghnet-integrating-near-and-long-range","title":"HoughNet: Integrating near and long-range evidence for bottom-up object detection","date":"2020-07-05","arxiv_id":"2007.02355","n_code_links":2,"syntology":null},{"paper":"/paper/bottom-up-object-detection-by-grouping","title":"Bottom-up Object Detection by Grouping Extreme and Center Points","date":"2019-01-23","arxiv_id":"1901.08043","n_code_links":2,"syntology":{"ran":1,"of":5,"unverified":4,"pointer_only":0}}],"papers_shown":2,"tasks":[{"task":"/task/object","name":"Object","papers":2},{"task":"/task/object-detection","name":"Object Detection","papers":2},{"task":"/task/object-detection-1","name":"object-detection","papers":2},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/keypoint-estimation","name":"Keypoint Estimation","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2019","papers":1},{"year":"2020","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/extremenet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}