{"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/contextlocnet-context-aware-deep-network","title":"ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization","arxiv_id":"1609.04331","date":"2016-09-14","proceeding":null,"authors":["Vadim Kantorov","Maxime Oquab","Minsu Cho","Ivan Laptev"],"abstract":"We aim to localize objects in images using image-level supervision only.\nPrevious approaches to this problem mainly focus on discriminative object\nregions and often fail to locate precise object boundaries. We address this\nproblem by introducing two types of context-aware guidance models, additive and\ncontrastive models, that leverage their surrounding context regions to improve\nlocalization. The additive model encourages the predicted object region to be\nsupported by its surrounding context region. The contrastive model encourages\nthe predicted object region to be outstanding from its surrounding context\nregion. Our approach benefits from the recent success of convolutional neural\nnetworks for object recognition and extends Fast R-CNN to weakly supervised\nobject localization. Extensive experimental evaluation on the PASCAL VOC 2007\nand 2012 benchmarks shows hat our context-aware approach significantly improves\nweakly supervised localization and detection.","url_abs":"http://arxiv.org/abs/1609.04331v1","url_pdf":"http://arxiv.org/pdf/1609.04331v1.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":"contextlocnet-context-aware-deep-network","repo_url":"https://github.com/vadimkantorov/contextlocnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"weakly-supervised-object-detection","task_name":"Weakly Supervised Object Detection"},{"task_slug":"weakly-supervised-object-localization","task_name":"Weakly-Supervised Object Localization"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fast-r-cnn","method_name":"Fast R-CNN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-object-detection-on-4","task":"Weakly Supervised Object Detection","dataset":"Charades","model":"ContextLocNet","rank_in_archive_order":4,"of":6,"metrics":{"MAP":"1.12"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal-1","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2007","model":"WSDDN + context","rank_in_archive_order":39,"of":41,"metrics":{"MAP":"36.3"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2012 test","model":"WSDDN + context","rank_in_archive_order":31,"of":32,"metrics":{"MAP":"35.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.04331","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}