{"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/adaptively-denoising-proposal-collection","title":"Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization","arxiv_id":null,"date":"2019-10-04","proceeding":"arXiv 2019 10","authors":["Wenju Xu","Yuanwei Wu","Wenchi Ma","Guanghui Wang"],"abstract":"In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations. The proposed approach is built on theobservation that the proposal set from the training dataset is a collectionof background, object parts, and objects. Several strategies are taken toadaptively eliminate the noisy proposals and generate pseudo object-levelannotations for the weakly labeled dataset. A multiple instance learning(MIL) algorithm enhanced by mask-out strategy is adopted to collect theclass-specific  object  proposals,  which are  then  utilized to  adapt  a pre-trained  classification  network  to  a  detection  network.  In  addition,  thedetection results from the detection network are re-weighted by jointlyconsidering the detection scores and the overlap ratio of proposals in aproposal subset optimization framework. The optimal proposals work asobject-level labels that enable a pseudo-strongly supervised dataset fortraining the detection network. Consequently, we establish a fully adap-tive detection network. Extensive evaluations on the PASCAL VOC 2007and 2012 datasets demonstrate a significant improvement compared withthe state-of-the-art methods.","url_abs":"https://arxiv.org/1910.02101","url_pdf":"https://arxiv.org/pdf/1910.02101.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":[],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"weakly-supervised-object-detection","task_name":"Weakly Supervised Object Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal-1","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2007","model":"Our scheme","rank_in_archive_order":35,"of":41,"metrics":{"MAP":"40.9"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2012 test","model":"Our scheme","rank_in_archive_order":32,"of":32,"metrics":{"MAP":"35.2"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}