{"url":"/method/grid-r-cnn","slug":"grid-r-cnn","name":"Grid R-CNN","full_name":"Grid R-CNN","full_name_withheld":false,"description_markdown":"**Grid R-CNN** is an object detection framework, where the traditional regression\r\nformulation is replaced by a grid point guided localization mechanism.\r\n\r\nGrid R-CNN divides the object bounding box region into grids and employs a fully convolutional network ([FCN](https://paperswithcode.com/method/fcn)) to predict the locations of grid points. Owing to the position sensitive property of fully convolutional architecture, Grid R-CNN maintains the explicit spatial information and grid points locations can be obtained in pixel level. When a certain number of grid points at specified location are known, the corresponding bounding box is definitely determined. Guided by the grid points, Grid R-CNN can determine more accurate object bounding box than regression method which lacks the guidance of explicit spatial information.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Grid R-CNN","paper":"/paper/grid-r-cnn","first_author":"Xin Lu","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/grid-r-cnn"},"source":{"url":"http://arxiv.org/abs/1811.12030v1","title":"Grid R-CNN","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/STVIR/Grid-R-CNN","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":4,"archive_num_papers":4,"papers_newest_first":[{"paper":null,"title":"Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery","date":"2024-07-27","arxiv_id":"2407.19184","n_code_links":0,"syntology":null},{"paper":"/paper/cpm-r-cnn-calibrating-point-guided","title":"CPM R-CNN: Calibrating Point-guided Misalignment in Object Detection","date":"2020-03-07","arxiv_id":"2003.03570","n_code_links":1,"syntology":null},{"paper":"/paper/grid-r-cnn-plus-faster-and-better","title":"Grid R-CNN Plus: Faster and Better","date":"2019-06-13","arxiv_id":"1906.05688","n_code_links":2,"syntology":null},{"paper":"/paper/grid-r-cnn","title":"Grid R-CNN","date":"2018-11-29","arxiv_id":"1811.12030","n_code_links":2,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":0}}],"papers_shown":4,"tasks":[{"task":"/task/object-detection","name":"Object Detection","papers":4},{"task":"/task/object-detection-1","name":"object-detection","papers":3},{"task":"/task/2d-object-detection","name":"2D Object Detection","papers":1},{"task":"/task/fire-detection","name":"Fire Detection","papers":1},{"task":"/task/management","name":"Management","papers":1},{"task":"/task/novel-object-detection","name":"Novel Object Detection","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-localization","name":"Object Localization","papers":1},{"task":"/task/regression-1","name":"regression","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2018","papers":1},{"year":"2019","papers":1},{"year":"2020","papers":1},{"year":"2024","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/grid-r-cnn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}