{"type":"ScholarlyArticle","title":"Complete Instances Mining for Weakly Supervised Instance Segmentation","authors":[{"name":"Zecheng Li","slug":"zecheng-li","given":"Zecheng","family":"Li","affiliation":"South China University of Technology"},{"name":"Zening Zeng","slug":"zening-zeng","given":"Zening","family":"Zeng","affiliation":"South China University of Technology"},{"name":"Yuqi Liang","slug":"yuqi-liang","given":"Yuqi","family":"Liang","affiliation":"South China University of Technology"},{"name":"Jin-Gang Yu","slug":"jin-gang-yu","given":"Jin-Gang","family":"Yu","affiliation":"South China University of Technology; Pazhou Laboratory"}],"abstract":"Weakly supervised instance segmentation (WSIS) using only image-level labels is a challenging task due to the difficulty of aligning coarse annotations with the finer task. However, with the advancement of deep neural networks (DNNs), WSIS has garnered significant attention. Following a proposal-based paradigm, we encounter a redundant segmentation problem resulting from a single instance being represented by multiple proposals. For example, we feed a picture of a dog and proposals into the network and expect to output only one proposal containing a dog, but the network outputs multiple proposals. To address this problem, we propose a novel approach for WSIS that focuses on the online refinement of complete instances through the use of MaskIoU heads to predict the integrity scores of proposals and a Complete Instances Mining (CIM) strategy to explicitly model the redundant segmentation problem and generate refined pseudo labels. Our approach allows the network to become aware of multiple instances and complete instances, and we further improve its robustness through the incorporation of an Anti-noise strategy. Empirical evaluations on the PASCAL VOC 2012 and MS COCO datasets demonstrate that our method achieves state-of-the-art performance with a notable margin. Our implementation will be made available at https://github.com/ZechengLi19/CIM.","doi":"10.24963/ijcai.2023/127","url":"https://ijcai.org/proceedings/2023/127","pdf":"https://ijcai.org/proceedings/2023/0127.pdf","markdown":"https://ijcai.org/proceedings/2023/127.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/127","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":127,"pages":{"first":1142,"last":1150},"keywords":["Machine Learning: ML: Weakly supervised learning","Computer Vision: CV: Segmentation","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Ahn et al., 2019] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. 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