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100 1 _aLiu, Weibin.
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245 1 0 _aGraph Neural Network Methods and Applications in Scene Understanding
_h[electronic resource] /
_cby Weibin Liu, Huaqing Hao, Hui Wang, Zhiyuan Zou, Weiwei Xing.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIV, 219 p. 47 illus., 46 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
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338 _aonline resource
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505 0 _aIntroduction -- Scene understanding -- Graph neural network basics -- Graph convolutional network for scene parsing -- Graph neural network for human parsing -- Dynamic graph neural networks for human parsing -- Graph neural networks for video object segmentation -- Conclusion and future work.
520 _aThe 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aEngineering mathematics.
650 0 _aArtificial intelligence.
650 1 4 _aComputational Intelligence.
650 2 4 _aEngineering Mathematics.
650 2 4 _aArtificial Intelligence.
700 1 _aHao, Huaqing.
_eauthor.
_4aut
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700 1 _aWang, Hui.
_eauthor.
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_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZou, Zhiyuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aXing, Weiwei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819799329
776 0 8 _iPrinted edition:
_z9789819799343
776 0 8 _iPrinted edition:
_z9789819799350
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-97-9933-6
912 _aZDB-2-INR
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