Graph Neural Network Methods And Applications In Scene Understanding
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Graph Neural Network Methods And Applications In Scene Understanding
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Author : Weibin Liu
language : en
Publisher: Springer Nature
Release Date : 2025-01-03
Graph Neural Network Methods And Applications In Scene Understanding written by Weibin Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
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.
Bio Inspired Computing Theories And Applications
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Author : Linqiang Pan
language : en
Publisher: Springer Nature
Release Date : 2024-04-15
Bio Inspired Computing Theories And Applications written by Linqiang Pan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-15 with Computers categories.
The two-volume set CCIS 2061 and 2062 constitutes the refereed post-conference proceedings of the 18th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2023, held in Changsha, China, during December 15–17, 2023. The 64 revised full papers presented in these proceedings were carefully reviewed and selected from 168 submissions. The papers are organized in the following topical sections: Volume I: Evolutionary Computation and Swarm Intelligence; and Membrane Computing and DNA Computing Volume II: Machine Learning and Applications; and Intelligent Control and Application.
Graph Neural Networks Essentials And Use Cases
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Author : Pethuru Raj Chelliah
language : en
Publisher: Springer Nature
Release Date : 2025-07-25
Graph Neural Networks Essentials And Use Cases written by Pethuru Raj Chelliah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.
This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals—and how they complement advances in generative, agentic, and physical AI—this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data—complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node’s local neighborhood by aggregating information from adjacent nodes, updating the node’s representation in the process. Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance. Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
Digital Technologies And Applications
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Author : Saad Motahhir
language : en
Publisher: Springer Nature
Release Date : 2023-04-28
Digital Technologies And Applications written by Saad Motahhir and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-28 with Technology & Engineering categories.
This book presents volume 1 of selected research papers presented at the third International Conference on Digital Technologies and Applications (ICDTA 23). This book highlights the latest innovations in digital technologies as: artificial intelligence, Internet of things, embedded systems, network technology, digital transformation and their applications in several areas as Industry 4.0, renewable energy, mechatronics, digital healthcare. The respective papers encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
Computer Vision And Image Processing Methods And Applications
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Author : Dr. Parthasarathi De
language : en
Publisher: Chyren Publication
Release Date : 2025-03-12
Computer Vision And Image Processing Methods And Applications written by Dr. Parthasarathi De and has been published by Chyren Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-12 with Antiques & Collectibles categories.
Multimodal Scene Understanding
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Author : Michael Ying Yang
language : en
Publisher: Academic Press
Release Date : 2019-07-16
Multimodal Scene Understanding written by Michael Ying Yang and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-16 with Technology & Engineering categories.
Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning
Handbook Of Research On Ai Methods And Applications In Computer Engineering
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Author : Kaddoura, Sanaa
language : en
Publisher: IGI Global
Release Date : 2023-01-30
Handbook Of Research On Ai Methods And Applications In Computer Engineering written by Kaddoura, Sanaa and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-30 with Computers categories.
The development of artificial intelligence (AI) involves the creation of computer systems that can do activities that would ordinarily require human intelligence, such as visual perception, speech recognition, decision making, and language translation. Through increasingly complex programming approaches, it has been transforming and advancing the discipline of computer science. The Handbook of Research on AI Methods and Applications in Computer Engineering illuminates how today’s computer engineers and scientists can use AI in real-world applications. It focuses on a few current and emergent AI applications, allowing a more in-depth discussion of each topic. Covering topics such as biomedical research applications, navigation systems, and search engines, this premier reference source is an excellent resource for computer scientists, computer engineers, IT managers, students and educators of higher education, librarians, researchers, and academicians.
Graph Neural Networks Foundations Frontiers And Applications
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Author : Lingfei Wu
language : en
Publisher: Springer Nature
Release Date : 2022-01-03
Graph Neural Networks Foundations Frontiers And Applications written by Lingfei Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-03 with Computers categories.
Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Advanced Intelligent Computing Technology And Applications
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Author : De-Shuang Huang
language : en
Publisher: Springer Nature
Release Date : 2024-08-12
Advanced Intelligent Computing Technology And Applications written by De-Shuang Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-12 with Computers categories.
This 13-volume set LNCS 14862-14874 constitutes - in conjunction with the 6-volume set LNAI 14875-14880 and the two-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
Concepts And Techniques Of Graph Neural Networks
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Author : Kumar, Vinod
language : en
Publisher: IGI Global
Release Date : 2023-05-22
Concepts And Techniques Of Graph Neural Networks written by Kumar, Vinod and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-22 with Computers categories.
Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.