{"type":"ScholarlyArticle","title":"Self-Classification Enhancement and Correction for Weakly Supervised Object Detection","authors":[{"name":"Yufei Yin","slug":"yufei-yin","given":"Yufei","family":"Yin","affiliation":""},{"name":"Lechao Cheng","slug":"lechao-cheng","given":"Lechao","family":"Cheng","affiliation":""},{"name":"Wengang Zhou","slug":"wengang-zhou","given":"Wengang","family":"Zhou","affiliation":""},{"name":"Jiajun Deng","slug":"jiajun-deng","given":"Jiajun","family":"Deng","affiliation":""},{"name":"Zhou Yu","slug":"zhou-yu","given":"Zhou","family":"Yu","affiliation":""},{"name":"Houqiang Li","slug":"houqiang-li","given":"Houqiang","family":"Li","affiliation":""}],"abstract":"In recent years, weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost. The success of recent WSOD models is often ascribed to the two-stage multi-class classification (MCC) task, i.e., multiple instance learning and online classification refinement.  Despite  achieving non-trivial progresses, these methods overlook potential classification ambiguities between these two  MCC tasks and fail to leverage their unique strengths.  In this work, we introduce a novel WSOD framework to ameliorate these two issues. For one thing, we propose a self-classification enhancement module that integrates intra-class binary classification (ICBC) to bridge the gap between the two distinct MCC tasks. The ICBC task enhances the network’s discrimination between positive and mis-located samples in a class-wise manner and forges a mutually reinforcing relationship with the MCC task. For another, we propose a self-classification correction algorithm during inference, which combines the results of both MCC tasks to effectively reduce the mis-classified predictions. Extensive experiments on the prevalent VOC 2007 & 2012 datasets demonstrate the superior performance of our framework.","doi":"10.24963/ijcai.2025/254","url":"https://ijcai.org/proceedings/2025/254","pdf":"https://ijcai.org/proceedings/2025/0254.pdf","markdown":"https://ijcai.org/proceedings/2025/254.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/254","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":254,"pages":{"first":2278,"last":2286},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}