ISBN: 9781394356331 - Graph Convolutional Neural Networks for Computer Vision (14 results)
- Hardcover
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Graph Convolutional Neural Networks for Computer Vision
Alagarsamy, Malini (EDT); Dhanaraj, Rajesh Kumar (EDT); Lilian, J. Felicia (EDT); Sharma, Vandana (EDT); Ghinea, George (EDT)
- Hardcover
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- Hardcover
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Graph Convolutional Neural Networks for Computer Vision
Alagarsamy, Malini (EDT); Dhanaraj, Rajesh Kumar (EDT); Lilian, J. Felicia (EDT); Sharma, Vandana (EDT); Ghinea, George (EDT)
- Hardcover
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Graph Convolutional Neural Networks for Computer Vision
Alagarsamy, Malini (EDT); Dhanaraj, Rajesh Kumar (EDT); Lilian, J. Felicia (EDT); Sharma, Vandana (EDT); Ghinea, George (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Graph Convolutional Neural Networks for Computer Vision
Alagarsamy, Malini (EDT); Dhanaraj, Rajesh Kumar (EDT); Lilian, J. Felicia (EDT); Sharma, Vandana (EDT); Ghinea, George (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Hardcover
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- Hardcover
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- Softcover
Seller: moluna, Greven, Germanymoluna
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- Hardcover
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- Hardcover
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Condition: New. 1st edition NO-PA16APR2015-KAP.
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 352 pages. 7.87x10.24x1.97 inches. In Stock.
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. Neuware - 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.…
- Hardcover
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