03900nam a22005655i 4500 978-3-031-59811-1 DE-He213 20251030112243.0 cr nn 008mamaa 240530s2024 sz | s |||| 0|eng d 9783031598111 978-3-031-59811-1 10.1007/978-3-031-59811-1 doi TA345-345.5 UN bicssc COM018000 bisacsh UN thema Chen, Yen-Wei. author. aut http://id.loc.gov/vocabulary/relators/aut 77837 Recent Advances in Logo Detection Using Machine Learning Paradigms [electronic resource] : Theory and Practice / by Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XII, 119 p. 64 illus., 63 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Intelligent Systems Reference Library, 1868-4408 ; 255 Deep Convolutional Neural networks -- Introduction to Logo Detection -- Weakly Supervised Logo Detection Approach. This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues. This book provides numerous ways that deep learners can use for logo recognition, including: Deep learning-based end-to-end trainable architecture for logo detection Weakly supervised logo recognition approach using attention mechanisms Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images Approach for logo detection modelingdomain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem. The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks. The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks. . Engineering Data processing. Computational intelligence. 64052 Artificial intelligence. 58441 Data Engineering. 66059 Computational Intelligence. 64052 Artificial Intelligence. 58441 Ruan, Xiang. author. aut http://id.loc.gov/vocabulary/relators/aut 77838 Jain, Rahul Kumar. author. aut http://id.loc.gov/vocabulary/relators/aut 77839 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031598104 Printed edition: 9783031598128 Printed edition: 9783031598135 Intelligent Systems Reference Library, 1868-4408 ; 255 67034 https://doi.org/10.1007/978-3-031-59811-1 ZDB-2-INR ZDB-2-SXIT 60293 60293