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Free Download Weibin Liu, "Graph Neural Network Methods and Applications in Scene Understanding"
English | ISBN: 9819799325 | 2025 | 233 pages | MOBI | 22 MB
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.

Free Download Weibin Liu, "Graph Neural Network Methods and Applications in Scene Understanding"
English | ISBN: 9819799325 | 2025 | 233 pages | AZW3 | 44 MB
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.

Free Download Kerstin Jacobsson, "Governing Street-Level Bureaucracies "
English | ISBN: 1032331941 | 2025 | 166 pages | EPUB | 1027 KB
This book examines how caseworkers are governed in today's street-level bureaucracies. It redefines our understanding of public sector governance by highlighting the subtle, informal, and everyday forms of organizational governance that shape caseworkers' subjectivities beyond formal policies and professional identities. Based on four distinct types of normative governance - 'governance by discourse', 'governance by emotions', 'governance by peers', and 'governance by numbers, colours, and symbols', the book shows how caseworkers are shaped as organizational staff members alongside their roles as welfare professionals and welfare state bureaucrats.

Free Download Irene Liu, "Goodness and Tradition "
English | ISBN: 1032893451 | 2025 | 210 pages | EPUB | 830 KB
This book investigates the importance of spirituality in moral life. The author claims that modern secular morality suffers from a lack of spirituality and argues that a solution to this problem can be found in tradition.

Graph Neural Network Methods and Applications in Scene Understanding (MOBI)

Graph Neural Network Methods and Applications in Scene Understanding (AZW3)

Governing Street-Level Bureaucracies

Goodness and Tradition

Goliath's Curse The History and Future of Societal Collapse

Goldstücke - Die Hans im Glück Burger, Bowls und Drinks

Going Underground American Punk 1979-1989

Going Potty

Going Infinite The Rise and Fall of a New Tycoon

Going Downtown The US Air Force over Vietnam, Laos and Cambodia, 1961-75