Graph Convolutional Neural Networks for Computer Vision - Rilegato

Rilegato

ISBN 10: 1394356331 ISBN 13: 9781394356331

Casa editrice: Wiley-Scrivener, 2025

Riassunto

Revolutionize your machine learning practice with this essential book that provides expert insights into leveraging Graph Convolutional Networks (GCNNs) to overcome the limitations of traditional CNNs.

In the last decade, computer vision has become a major focus for addressing the world's growing processing needs. Many existing deep learning architectures for computer vision challenges are based on convolutional neural networks (CNNs). Despite their great achievements, CNNs struggle to encode the intrinsic graph patterns in specific learning tasks. In contrast, graph convolutional networks have been used to address several computer vision issues with equivalent or superior results. The use of GCNNs has shown significant achievement in image classifications, video understanding, point clouds, meshes, and other applications in natural language processing. This book focuses on the applications of graph convolutional networks in computer vision. Through expert insights, it explores how researchers are finding ways to perform convolution algorithms on graphs to improve the way we use machine learning.

Dettagli dell'edizione

• Editore: Wiley-Scrivener
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 1394356331
• ISBN 13: 9781394356331
• Rilegatura: Copertina rigida
• Numero di pagine: 352
• Redattore: Alagarsamy Malini, Dhanaraj Rajesh Kumar, Lilian J. Felicia, Sharma Vandana, Ghinea George
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile