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024 7 _a10.1007/978-981-97-9933-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
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100 1 _aLiu, Weibin.
_eauthor.
_0(orcid)0000-0001-6246-0051
_1https://orcid.org/0000-0001-6246-0051
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990129
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
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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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.
650 0 _aComputational intelligence.
_964052
650 0 _aEngineering mathematics.
_964145
650 0 _aArtificial intelligence.
_958441
650 1 4 _aComputational Intelligence.
_964052
650 2 4 _aEngineering Mathematics.
_964145
650 2 4 _aArtificial Intelligence.
_958441
700 1 _aHao, Huaqing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990130
700 1 _aWang, Hui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_977746
700 1 _aZou, Zhiyuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990131
700 1 _aXing, Weiwei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990132
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789819799343
776 0 8 _iPrinted edition:
_z9789819799350
856 4 0 _uhttps://doi.org/10.1007/978-981-97-9933-6
912 _aZDB-2-INR
912 _aZDB-2-SXIT
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