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Titlebook: Recent Advances in Logo Detection Using Machine Learning Paradigms; Theory and Practice Yen-Wei Chen,Xiang Ruan,Rahul Kumar Jain Book 2024

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發(fā)表于 2025-3-21 16:59:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Recent Advances in Logo Detection Using Machine Learning Paradigms
副標(biāo)題Theory and Practice
編輯Yen-Wei Chen,Xiang Ruan,Rahul Kumar Jain
視頻videohttp://file.papertrans.cn/823/822832/822832.mp4
概述Presents the novel logo detection methods using machine learning paradigms.Demonstrates the merits of the presented approaches over the reported approaches using the real-world applications.? Includes
叢書(shū)名稱Intelligent Systems Reference Library
圖書(shū)封面Titlebook: Recent Advances in Logo Detection Using Machine Learning Paradigms; Theory and Practice Yen-Wei Chen,Xiang Ruan,Rahul Kumar Jain Book 2024
描述.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 modeling domain 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 analys
出版日期Book 2024
關(guān)鍵詞Intelligent Systems; Deep Learning; Anchorless/Anchorfree Object Detectors; Feature Extraction; Attentio
版次1
doihttps://doi.org/10.1007/978-3-031-59811-1
isbn_softcover978-3-031-59813-5
isbn_ebook978-3-031-59811-1Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Yen-Wei Chen,Xiang Ruan,Rahul Kumar Jain example, are common disorders with significant adverse health consequences. Sleep apnea is associated with increased cardiovascular mortality, impaired quality of life and increased motor vehicle accidents. In addition, sleep apnea often co-exists with other chronic conditions including obesity, th
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Yen-Wei Chen,Xiang Ruan,Rahul Kumar Jainbased review of sleep medicine for the pulmonologist and oth.Sleep disorders represent a major portion of the chief complaints seen by pulmonologists and other physicians.? Sleep apnea and hypopnea syndrome for example, are common disorders with significant adverse health consequences. Sleep apnea i
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Yen-Wei Chen,Xiang Ruan,Rahul Kumar Jainbased review of sleep medicine for the pulmonologist and oth.Sleep disorders represent a major portion of the chief complaints seen by pulmonologists and other physicians.? Sleep apnea and hypopnea syndrome for example, are common disorders with significant adverse health consequences. Sleep apnea i
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Yen-Wei Chen,Xiang Ruan,Rahul Kumar Jain example, are common disorders with significant adverse health consequences. Sleep apnea is associated with increased cardiovascular mortality, impaired quality of life and increased motor vehicle accidents. In addition, sleep apnea often co-exists with other chronic conditions including obesity, th
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Weakly Supervised Logo Detection Approach,o recognition. Most existing logo detection methods often rely on precise object-level bounding box (position bounding box) annotations, that poses substantial challenges in practical settings due to the labor-intensive nature of object-level annotations. To address this issue, this chapter presents
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