{"type":"ScholarlyArticle","title":"FGNet: Towards Filling the Intra-class and Inter-class Gaps for Few-shot Segmentation","authors":[{"name":"Yuxuan Zhang","slug":"yuxuan-zhang","given":"Yuxuan","family":"Zhang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Wei Yang","slug":"wei-yang","given":"Wei","family":"Yang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Suzhou Institute for Advanced Research , University of Science and Technology of China; Hefei National Laboratory"},{"name":"Shaowei Wang","slug":"shaowei-wang","given":"Shaowei","family":"Wang","affiliation":"Institute of Artificial Intelligence and Blockchain , Guangzhou University"}],"abstract":"Current few-shot segmentation (FSS) approaches have made tremendous achievements based on prototypical learning techniques. However, due to the scarcity of the support data provided, FSS methods still suffer from the intra-class and inter-class gaps. In this paper, we propose a uniform network to fill both the gaps, termed FGNet. It consists of the novel design of a Self-Adaptive Module (SAM) to emphasize the query feature to generate an enhanced prototype for self-alignment. Such a prototype caters to each query sample itself since it contains the underlying intra-instance information, which gets around the intra-class appearance gap. Moreover, we design an Inter-class Feature Separation Module (IFSM) to separate the feature space of the target class from other classes, which contributes to bridging the inter-class gap. In addition, we present several new losses and a method termed B-SLIC, which help to further enhance the separation performance of FGNet. Experimental results show that FGNet reduces both the gaps for FSS by SAM and IFSM respectively, and achieves state-of-the-art performances on both PASCAL-5i and COCO-20i datasets compared with previous top-performing approaches.","doi":"10.24963/ijcai.2023/194","url":"https://ijcai.org/proceedings/2023/194","pdf":"https://ijcai.org/proceedings/2023/0194.pdf","markdown":"https://ijcai.org/proceedings/2023/194.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/194","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":194,"pages":{"first":1749,"last":1758},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"1-shot 5-shot Method split-0 split-1 split-2 split-3 mean split-0 split-1 split-2 split-3 mean PPNet [Liu et al., 2020] 52.7 62.8 57.4 47.7 55.2 60.3 70.0 69.4 60.7 65.1 PFENet [Tian et al., 2020] 60.5 69.4 54.4 55.9 60.1 62.8 70.4 54.9 57.6 61.4 ASGNet [Li et al., 2021] 59.8 67.4 55.6 54.4 59.3 64.6 71.3 64.2 57.3 64.4 MLC [Yang et al., 2021] 60.8 71.3 61.5 56.9 62.6 65.8 74.9 71.4 63.1 68.8 HSNet [Min et al., 2021] 67.3 72.3 62.0 63.1 66.2 71.8 74.4 67.0 68.3 70.4 SSP [Fan et al., 2022] 63.7 70.1 66.7 55.4 64.0 70.3 76.3 77.8 65.5 72.5 IPRNet [Okazawa, 2022] 67.8 74.6 65.7 62.2 67.5 70.0 75.9 71.8 65.8 70.9 FGNet (Ours) 69.4 73.8 68.3 62.8 68.6 72.8 75.7 79.4 65.3 73.3 Table 1: Performance on PASCAL-5 i in MIoU with per-split results under 1-shot and 5-shot settings, using the backbone of ResNet-101. 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