{"type":"ScholarlyArticle","title":"Dichotomous Image Segmentation with Frequency Priors","authors":[{"name":"Yan Zhou","slug":"yan-zhou","given":"Yan","family":"Zhou","affiliation":"State Key Laboratory of Industrial Control Technology , College of Control Science and Engineering , Zhejiang University , China; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology , School of Computer Science and Engineering , Northwestern Polytechnical University , China"},{"name":"Bo Dong","slug":"bo-dong","given":"Bo","family":"Dong","affiliation":"College of Biomedical Engineering and Instrumental Science , Zhejiang University , China"},{"name":"Yuanfeng Wu","slug":"yuanfeng-wu","given":"Yuanfeng","family":"Wu","affiliation":"Zhejiang Lab , China"},{"name":"Wentao Zhu","slug":"wentao-zhu","given":"Wentao","family":"Zhu","affiliation":"Zhejiang Lab , China"},{"name":"Geng Chen","slug":"geng-chen","given":"Geng","family":"Chen","affiliation":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology , School of Computer Science and Engineering , Northwestern Polytechnical University , China"},{"name":"Yanning Zhang","slug":"yanning-zhang","given":"Yanning","family":"Zhang","affiliation":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology , School of Computer Science and Engineering , Northwestern Polytechnical University , China"}],"abstract":"Dichotomous image segmentation (DIS) has a wide range of real-world applications and gained increasing research attention in recent years. In this paper, we propose to tackle DIS with informative frequency priors. Our model, called FP-DIS, stems from the fact that prior knowledge in the frequency domain can provide valuable cues to identify fine-grained object boundaries. Specifically, we propose a frequency prior generator to jointly utilize a fixed filter and learnable filters to extract informative frequency priors. Before embedding the frequency priors into the network, we first harmonize the multi-scale side-out features to reduce their heterogeneity. This is achieved by our feature harmonization module, which is based on a gating mechanism to harmonize the grouped features. Finally, we propose a frequency prior embedding module to embed the frequency priors into multi-scale features through an adaptive modulation strategy. Extensive experiments on the benchmark dataset, DIS5K, demonstrate that our FP-DIS outperforms state-of-the-art methods by a large margin in terms of key evaluation metrics.","doi":"10.24963/ijcai.2023/202","url":"https://ijcai.org/proceedings/2023/202","pdf":"https://ijcai.org/proceedings/2023/0202.pdf","markdown":"https://ijcai.org/proceedings/2023/202.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/202","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":202,"pages":{"first":1822,"last":1830},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Scene analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Achanta et al., 2009] Radhakrishna Achanta, Sheila Hemami, Francisco Estrada, and Sabine Susstrunk. 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