{"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/self-produced-guidance-for-weakly-supervised","title":"Self-produced Guidance for Weakly-supervised Object Localization","arxiv_id":"1807.08902","date":"2018-07-24","proceeding":"ECCV 2018 9","authors":["Xiaolin Zhang","Yunchao Wei","Guoliang Kang","Yi Yang","Thomas Huang"],"abstract":"Weakly supervised methods usually generate localization results based on\nattention maps produced by classification networks. However, the attention maps\nexhibit the most discriminative parts of the object which are small and sparse.\nWe propose to generate Self-produced Guidance (SPG) masks which separate the\nforeground, the object of interest, from the background to provide the\nclassification networks with spatial correlation information of pixels. A\nstagewise approach is proposed to incorporate high confident object regions to\nlearn the SPG masks. The high confident regions within attention maps are\nutilized to progressively learn the SPG masks. The masks are then used as an\nauxiliary pixel-level supervision to facilitate the training of classification\nnetworks. Extensive experiments on ILSVRC demonstrate that SPG is effective in\nproducing high-quality object localizations maps. Particularly, the proposed\nSPG achieves the Top-1 localization error rate of 43.83% on the ILSVRC\nvalidation set, which is a new state-of-the-art error rate.","url_abs":"http://arxiv.org/abs/1807.08902v2","url_pdf":"http://arxiv.org/pdf/1807.08902v2.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":"self-produced-guidance-for-weakly-supervised","repo_url":"https://github.com/xiaomengyc/SPG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"weakly-supervised-object-localization","task_name":"Weakly-Supervised Object Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-object-localization-on-cub","task":"Weakly-Supervised Object Localization","dataset":"CUB-200-2011","model":"SPG","rank_in_archive_order":4,"of":10,"metrics":{"MaxBoxAccV2":"60.4","Top-1 Error Rate":"53.36","Top-5 Error":"42.28"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-localization-on","task":"Weakly-Supervised Object Localization","dataset":"ILSVRC 2015","model":"SPG","rank_in_archive_order":1,"of":2,"metrics":{"Top-1 Error Rate":"51.40"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-localization-on-1","task":"Weakly-Supervised Object Localization","dataset":"ILSVRC 2016","model":"SPG","rank_in_archive_order":1,"of":4,"metrics":{"Top-5 Error":"40.00"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.08902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08902"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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