Fine-Grained Image Analysis: Modern Approaches

Author:   Xiu-Shen Wei
Publisher:   Springer International Publishing AG
Edition:   2023 ed.
ISBN:  

9783031313769


Pages:   206
Publication Date:   05 July 2024
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Fine-Grained Image Analysis: Modern Approaches


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Overview

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.  

Full Product Details

Author:   Xiu-Shen Wei
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2023 ed.
ISBN:  

9783031313769


ISBN 10:   3031313763
Pages:   206
Publication Date:   05 July 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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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.

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