{"type":"ScholarlyArticle","title":"Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation","authors":[{"name":"Jiayi Chen","slug":"jiayi-chen","given":"Jiayi","family":"Chen","affiliation":"Nanjing University of Aeronautics and Astronautics; State Key Laboratory of Integrated Services Networks , Xidian University"},{"name":"Rong Quan","slug":"rong-quan","given":"Rong","family":"Quan","affiliation":"Nanjing University of Aeronautics and Astronautics; State Key Laboratory of Integrated Services Networks , Xidian University"},{"name":"Jie Qin","slug":"jie-qin","given":"Jie","family":"Qin","affiliation":"Nanjing University of Aeronautics and Astronautics; State Key Laboratory of Integrated Services Networks , Xidian University"}],"abstract":"Cross-Domain Few-shot Semantic Segmentation (CD-FSS) aims to train generalized models that can segment classes from different domains with a few labeled images. Previous works have proven the effectiveness of feature transformation in addressing CD-FSS. However, they completely rely on support images for feature transformation, and repeatedly utilizing a few support images for each class may easily lead to overfitting and overlooking intra-class appearance differences. In this paper, we propose a Doubly Matching Transformation-based Network (DMTNet) to solve the above issue. Instead of completely relying on support images, we propose Self-Matching Transformation (SMT) to construct query-specific transformation matrices based on query images themselves to transform domain-specific query features into domain-agnostic ones. Calculating query-specific transformation matrices can prevent overfitting, especially for the meta-testing stage where only one or several images are used as support images to segment hundreds or thousands of images. After obtaining domain-agnostic features, we exploit a Dual Hypercorrelation Construction (DHC) module to explore the hypercorrelations between the query image with the foreground and background of the support image, based on which foreground and background prediction maps are generated and supervised, respectively, to enhance the segmentation result. In addition, we propose a Test-time Self-Finetuning (TSF) strategy to more accurately self-tune the query prediction in unseen domains. Extensive experiments on four popular datasets show that DMTNet achieves superior performance over state-of-the-art approaches. Code is available at https://github.com/ChenJiayi68/DMTNet.","doi":"10.24963/ijcai.2024/71","url":"https://ijcai.org/proceedings/2024/71","pdf":"https://ijcai.org/proceedings/2024/0071.pdf","markdown":"https://ijcai.org/proceedings/2024/71.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/71","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":71,"pages":{"first":641,"last":649},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Boudiaf et al., 2020] Malik Boudiaf, Hoel Kervadec, Im- tiaz Masud Ziko, Pablo Piantanida, Ismail Ben Ayed, and José Dolz. 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