{"type":"ScholarlyArticle","title":"Structure-Aware Spatial-Temporal Interaction Network for Video Shadow Detection","authors":[{"name":"Housheng Wei","slug":"housheng-wei","given":"Housheng","family":"Wei","affiliation":"National Key Laboratory of Fundamental Science on Synthetic Vision , Sichuan University"},{"name":"Guanyu Xing","slug":"guanyu-xing","given":"Guanyu","family":"Xing","affiliation":"National Key Laboratory of Fundamental Science on Synthetic Vision , Sichuan University"},{"name":"Jingwei Liao","slug":"jingwei-liao","given":"Jingwei","family":"Liao","affiliation":"Department of Information Sciences and Technology , George Mason University"},{"name":"Yanci Zhang","slug":"yanci-zhang","given":"Yanci","family":"Zhang","affiliation":"College of Computer Science , Sichuan University"},{"name":"Yanli Liu","slug":"yanli-liu","given":"Yanli","family":"Liu","affiliation":"College of Computer Science , Sichuan University"}],"abstract":"Video shadow detection faces significant challenges due to ambiguous semantics and variable shapes. Existing video shadow detection algorithms typically overlook the fine shadow details, resulting in inconsistent detection between consecutive frames in complex real-world video scenarios. To address this issue, we propose a spatial-temporal feature interaction strategy, which refines and enhances global shadow semantics with local prior features in the modeling of shadow relations between frames. Moreover, a structure-aware shadow prediction module is proposed, which focuses on modeling the distance relation between local shadow edges and regions. Quantitative experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods, providing stable and consistent shadow detection results in complex video shadow scenarios.","doi":"10.24963/ijcai.2024/158","url":"https://ijcai.org/proceedings/2024/158","pdf":"https://ijcai.org/proceedings/2024/0158.pdf","markdown":"https://ijcai.org/proceedings/2024/158.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/158","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":158,"pages":{"first":1425,"last":1433},"keywords":["Computer Vision: CV: Segmentation","Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Scene analysis and understanding","Computer Vision: CV: Video analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Adams et al., 2022] Haley Adams, Jeanine Stefanucci, Sarah Creem-Regehr, Grant Pointon, William Thompson, and Bobby Bodenheimer. 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