{"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/locnet-improving-localization-accuracy-for","title":"LocNet: Improving Localization Accuracy for Object Detection","arxiv_id":"1511.07763","date":"2015-11-24","proceeding":"CVPR 2016 6","authors":["Spyros Gidaris","Nikos Komodakis"],"abstract":"We propose a novel object localization methodology with the purpose of\nboosting the localization accuracy of state-of-the-art object detection\nsystems. Our model, given a search region, aims at returning the bounding box\nof an object of interest inside this region. To accomplish its goal, it relies\non assigning conditional probabilities to each row and column of this region,\nwhere these probabilities provide useful information regarding the location of\nthe boundaries of the object inside the search region and allow the accurate\ninference of the object bounding box under a simple probabilistic framework.\n  For implementing our localization model, we make use of a convolutional\nneural network architecture that is properly adapted for this task, called\nLocNet. We show experimentally that LocNet achieves a very significant\nimprovement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and\nthat it can be very easily coupled with recent state-of-the-art object\ndetection systems, helping them to boost their performance. Finally, we\ndemonstrate that our detection approach can achieve high detection accuracy\neven when it is given as input a set of sliding windows, thus proving that it\nis independent of box proposal methods.","url_abs":"http://arxiv.org/abs/1511.07763v2","url_pdf":"http://arxiv.org/pdf/1511.07763v2.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":"locnet-improving-localization-accuracy-for","repo_url":"https://github.com/gidariss/locnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.07763","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}