03458nam 22005653 450 991092100820332120251116213945.097898197993369819799333(CKB)37156192900041(MiAaPQ)EBC31875743(Au-PeEL)EBL31875743(OCoLC)1493040389(BIP)119747585(BIP)117954498(EXLCZ)993715619290004120250113d2025 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierGraph Neural Network Methods and Applications in Scene Understanding1st ed.Singapore :Springer,2025.©2024.1 online resource (0 pages)Intelligent Technologies and Robotics Series9789819799329 9819799325 The book focuses on graph neural network methods and applications for scene understanding. Graph Neural Network is an important method for graph-structured data processing, which has strong capability of graph data learning and structural feature extraction. Scene understanding is one of the research focuses in computer vision and image processing, which realizes semantic segmentation and object recognition of image or video. In this book, the algorithm, system design and performance evaluation of scene understanding based on graph neural networks have been studied. First, the book elaborates the background and basic concepts of graph neural network and scene understanding, then introduces the operation mechanism and key methodological foundations of graph neural network. The book then comprehensively explores the implementation and architectural design of graph neural networks for scene understanding tasks, including scene parsing, human parsing, and video object segmentation. The aim of this book is to provide timely coverage of the latest advances and developments in graph neural networks and their applications to scene understanding, particularly for readers interested in research and technological innovation in machine learning, graph neural networks and computer vision. Features of the book include self-supervised feature fusion based graph convolutional network is designed for scene parsing, structure-property based graph representation learning is developed for human parsing, dynamic graph convolutional network based on multi-label learning is designed for human parsing, and graph construction and graph neural network with transformer are proposed for video object segmentation.COMPUTERS / Artificial Intelligence / GeneralbisacshMATHEMATICS / AppliedbisacshTECHNOLOGY & ENGINEERING / Engineering (General)bisacshCOMPUTERS / Artificial Intelligence / GeneralMATHEMATICS / AppliedTECHNOLOGY & ENGINEERING / Engineering (General)006.31Liu Weibin1782239Hao Huaqing1782240Wang Hui426970Zou Zhiyuan1782241Xing Weiwei1067424MiAaPQMiAaPQMiAaPQBOOK9910921008203321Graph Neural Network Methods and Applications in Scene Understanding4308342UNINA