{"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/wsod2-learning-bottom-up-and-top-down","title":"WSOD2: Learning Bottom-up and Top-down Objectness Distillation forWeakly-supervised Object Detection","arxiv_id":null,"date":"2019-09-11","proceeding":"ICCV 2019 9","authors":["Zhaoyang Zeng","Bei Liu","Jianlong Fu","Hongyang Chao","Lei Zhang"],"abstract":"We study on weakly-supervised object detection (WSOD)which plays a vital role in relieving human involvement fromobject-level annotations.  Predominant works integrate re-gion proposal mechanisms with convolutional neural net-works (CNN). Although CNN is proficient in extracting dis-criminative  local  features,  grand  challenges  still  exist  tomeasure the likelihood of a bounding box containing a com-plete  object  (i.e.,  “objectness”).    In  this  paper,  we  pro-pose a novelWSODframework withObjectnessDistillation(i.e.,WSOD2) by designing a tailored training mechanismfor weakly-supervised object detection. Multiple regressiontargets  are  specifically  determined  by  jointly  consideringbottom-up (BU) and top-down (TD) objectness from low-level measurement and CNN confidences with an adaptivelinear  combination.   As  bounding  box  regression  can  fa-cilitate a region proposal learning to approach its regres-sion target with high objectness during training, deep ob-jectness  representation  learned  from  bottom-up  evidencescan be gradually distilled into CNN by optimization.   Weexplore  different  adaptive  training  curves  for  BU/TD  ob-jectness, and show that the proposed WSOD2can achievestate-of-the-art results.","url_abs":"https://arxiv.org/abs/1909.04972","url_pdf":"https://arxiv.org/pdf/1909.04972.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":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"region-proposal","task_name":"Region Proposal"},{"task_slug":"weakly-supervised-object-detection","task_name":"Weakly Supervised Object Detection"},{"task_slug":"object-detection-1","task_name":"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":"WSOD2","rank_in_archive_order":13,"of":41,"metrics":{"MAP":"53.6"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2012 test","model":"WSOD2","rank_in_archive_order":16,"of":32,"metrics":{"MAP":"47.2"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}