{"type":"ScholarlyArticle","title":"Low Shot Box Correction for Weakly Supervised Object Detection","authors":[{"name":"Tianxiang Pan","slug":"tianxiang-pan","given":"Tianxiang","family":"Pan","affiliation":"School of Software , Tsinghua University , China; Beijing National Research Center for Information Science and Technology (BNRist) , China"},{"name":"Bin Wang","slug":"bin-wang","given":"Bin","family":"Wang","affiliation":"School of Software , Tsinghua University , China; Beijing National Research Center for Information Science and Technology (BNRist) , China"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"School of Software , Tsinghua University , China; Beijing National Research Center for Information Science and Technology (BNRist) , China"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"WMG Data Science , University of Warwick , CV4 7AL Coventry , United Kingdom"},{"name":"Junhai Yong","slug":"junhai-yong","given":"Junhai","family":"Yong","affiliation":"School of Software , Tsinghua University , China; Beijing National Research Center for Information Science and Technology (BNRist) , China"}],"abstract":"Weakly supervised object detection (WSOD) has been widely studied but the accuracy of state-of-art methods remains far lower than strongly supervised methods. One major reason for this huge gap is the incomplete box detection problem which arises because most previous WSOD models are structured on classification networks and therefore tend to recognize the most discriminative parts instead of complete bounding boxes. To solve this problem, we define a low-shot weakly supervised object detection task and propose a novel low-shot box correction network to address it. The proposed task enables to train object detectors on a large data set all of which have image-level annotations, but only a small portion or few shots have box annotations. Given the low-shot box annotations, we use a novel box correction network to transfer the incomplete boxes into complete ones. Extensive empirical evidence shows that our proposed method yields state-of-art detection accuracy under various settings on the PASCAL VOC benchmark.","doi":"10.24963/ijcai.2019/125","url":"https://ijcai.org/proceedings/2019/125","pdf":"https://ijcai.org/proceedings/2019/0125.pdf","markdown":"https://ijcai.org/proceedings/2019/125.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/125","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Computer Vision","track":"Main track","number":125,"pages":{"first":890,"last":896},"keywords":["Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Computer Vision: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Bilen and Vedaldi, 2016] Hakan Bilen and Andrea Vedaldi. 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