{"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/denet-scalable-real-time-object-detection","title":"DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling","arxiv_id":"1703.10295","date":"2017-03-30","proceeding":"ICCV 2017 10","authors":["Lachlan Tychsen-Smith","Lars Petersson"],"abstract":"We define the object detection from imagery problem as estimating a very\nlarge but extremely sparse bounding box dependent probability distribution.\nSubsequently we identify a sparse distribution estimation scheme, Directed\nSparse Sampling, and employ it in a single end-to-end CNN based detection\nmodel. This methodology extends and formalizes previous state-of-the-art\ndetection models with an additional emphasis on high evaluation rates and\nreduced manual engineering. We introduce two novelties, a corner based\nregion-of-interest estimator and a deconvolution based CNN model. The resulting\nmodel is scene adaptive, does not require manually defined reference bounding\nboxes and produces highly competitive results on MSCOCO, Pascal VOC 2007 and\nPascal VOC 2012 with real-time evaluation rates. Further analysis suggests our\nmodel performs particularly well when finegrained object localization is\ndesirable. We argue that this advantage stems from the significantly larger set\nof available regions-of-interest relative to other methods. Source-code is\navailable from: https://github.com/lachlants/denet","url_abs":"http://arxiv.org/abs/1703.10295v3","url_pdf":"http://arxiv.org/pdf/1703.10295v3.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":"denet-scalable-real-time-object-detection","repo_url":"https://github.com/lachlants/denet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-pascal-voc-2007","task":"Object Detection","dataset":"PASCAL VOC 2007","model":"DeNet-101 (skip)","rank_in_archive_order":17,"of":30,"metrics":{"MAP":"77.1%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.10295","atlas_url":"https://app.syntology.ai/?focus=1703.10295","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}