Fine-Grained Image Analysis: Modern Approaches (Synthesis Lectures on Computer Vision)

Wei, Xiu-Shen

ISBN 10: 3031313763 / ISBN 13: 9783031313769

Edité par Springer, 2024

A propos de cet article

Zustand: Hervorragend | Seiten: 220 | Sprache: Englisch | Produktart: Bücher.

N° de réf. du vendeur 42977701/1

Détails bibliographiques

• Titre: Fine-Grained Image Analysis: Modern Approaches (Synthesis Lectures on Computer Vision)
• Éditeur: Springer
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 3031313763
• ISBN à 13 chiffres: 9783031313769
• Reliure: Couverture souple
• État de l'article: Hervorragend
• Catalogues du vendeur:
  • Bücher

A propos de ce titre

Synopsis

This book provides a comprehensive overview of the fine-grained image analysis research and modern approaches based on deep learning, spanning the full range of topics needed for designing operational fine-grained image systems. The author begins by providing detailed background information on FGIA, focusing on recognition and retrieval. The author also provides the fundamentals of convolutional neural networks to further make it easier for readers to understand the technical content in the book. The book introduces the main technical paradigms, technological developments, and representative approaches of fine-grained image recognition and fine-grained image retrieval. The author covers multiple popular research topics and includes cross-domain knowledge. The book also highlights advanced applications and topics for future research.  

À propos de l?auteur

Xiu-Shen Wei, Ph.D., is a Professor at Southeast University's School of Computer Science and Engineering. He received his Ph.D. degree from Nanjing University. Dr. Wei previously served as the Founding Director at Megvii Research Nanjing, Megvii Technology. He was also a visiting Scholar at The University of Adelaide. Dr. Wei's research interests include deep convolutional neural networks, fine-grained visual analysis, long-tailed distribution learning, general object detection, and weakly supervised learning.