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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,Mitigating Domain Shift in Logo Detection: An Adversarial Learning-Based Approach,s, we face a domain shift problem between the training data (source domain) and test data (target data) resulting in reduction of performance. The domain gab or domain shift problem is caused by the difference in feature distributions of training and test data. In practical scenarios, deploying trai

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,Unsupervised Logo Detection with Adversarial Domain Adaptation from Synthetic to Real Images,lustrated the effectiveness of convolutional neural networks (CNNs) trained on simulated or synthetic images for detecting objects in real-world images. Synthesized training images with automatically generated annotations at the object-level offer a promising alternative to the laborious and costly

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1868-4394 rted approaches using the real-world applications.​ Includes.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, a

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Recent Advances in Logo Detection Using Machine Learning Paradigms978-3-031-59811-1Series ISSN 1868-4394 Series E-ISSN 1868-4408

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