{"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/pcl-proposal-cluster-learning-for-weakly","title":"PCL: Proposal Cluster Learning for Weakly Supervised Object Detection","arxiv_id":"1807.03342","date":"2018-07-09","proceeding":null,"authors":["Peng Tang","Xinggang Wang","Song Bai","Wei Shen","Xiang Bai","Wenyu Liu","Alan Yuille"],"abstract":"Weakly Supervised Object Detection (WSOD), using only image-level annotations\nto train object detectors, is of growing importance in object recognition. In\nthis paper, we propose a novel deep network for WSOD. Unlike previous networks\nthat transfer the object detection problem to an image classification problem\nusing Multiple Instance Learning (MIL), our strategy generates proposal\nclusters to learn refined instance classifiers by an iterative process. The\nproposals in the same cluster are spatially adjacent and associated with the\nsame object. This prevents the network from concentrating too much on parts of\nobjects instead of whole objects. We first show that instances can be assigned\nobject or background labels directly based on proposal clusters for instance\nclassifier refinement, and then show that treating each cluster as a small new\nbag yields fewer ambiguities than the directly assigning label method. The\niterative instance classifier refinement is implemented online using multiple\nstreams in convolutional neural networks, where the first is an MIL network and\nthe others are for instance classifier refinement supervised by the preceding\none. Experiments are conducted on the PASCAL VOC, ImageNet detection, and\nMS-COCO benchmarks for WSOD. Results show that our method outperforms the\nprevious state of the art significantly.","url_abs":"http://arxiv.org/abs/1807.03342v2","url_pdf":"http://arxiv.org/pdf/1807.03342v2.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":"pcl-proposal-cluster-learning-for-weakly","repo_url":"https://github.com/ppengtang/oicr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pcl-proposal-cluster-learning-for-weakly","repo_url":"https://github.com/George-Holbrow-Wilshaw/Human-Protein-Atlas-Kaggle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"pcl-proposal-cluster-learning-for-weakly","repo_url":"https://github.com/ppengtang/pcl.pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pcl-proposal-cluster-learning-for-weakly","repo_url":"https://github.com/JoegameZhou/mPanGu-Alpha-53","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"weakly-supervised-object-detection","task_name":"Weakly Supervised Object Detection"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-object-detection-on-4","task":"Weakly Supervised Object Detection","dataset":"Charades","model":"PCL","rank_in_archive_order":2,"of":6,"metrics":{"MAP":"2.83"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-hico","task":"Weakly Supervised Object Detection","dataset":"HICO-DET","model":"PCL","rank_in_archive_order":2,"of":4,"metrics":{"MAP":"3.62"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on","task":"Weakly Supervised Object Detection","dataset":"ImageNet","model":"PCL-OB-G-Ens + FRCNN","rank_in_archive_order":1,"of":4,"metrics":{"MAP":"19.6"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal-1","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2007","model":"PCL-OB-G-Ens + FRCNN","rank_in_archive_order":22,"of":41,"metrics":{"MAP":"48.8"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal","task":"Weakly Supervised Object Detection","dataset":"PASCAL VOC 2012 test","model":"PCL-OB-G-Ens + FRCNN","rank_in_archive_order":20,"of":32,"metrics":{"MAP":"44.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.03342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03342"}},"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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