Graph Convolutional Neural Networks for Computer Vision by Ma... - 9781394356331

Graph Convolutional Neural Networks for Computer VisionAuthor(s): Malini Alagarsamy, Rajesh Kumar Dhanaraj, J. Felicia Lilian, Vandana Sharma, Gheorghita Ghinea\nFormat: Hardback\nPublisher: John Wiley & Sons Inc, United States\nImprint: Wiley-Scrivener\nISBN-13: 9781394356331, 978-1394356331\nSynopsis\nRevolutionize 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.\n\n 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 res.