Stress
发表于 2025-3-23 11:37:39
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相一致
发表于 2025-3-23 15:48:04
,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
固执点好
发表于 2025-3-23 18:28:22
,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
Buttress
发表于 2025-3-23 22:37:37
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
crumble
发表于 2025-3-24 03:11:01
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ADORN
发表于 2025-3-24 09:42:45
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RALES
发表于 2025-3-24 13:54:10
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缓和
发表于 2025-3-24 17:34:34
Recent Advances in Logo Detection Using Machine Learning Paradigms978-3-031-59811-1Series ISSN 1868-4394 Series E-ISSN 1868-4408
FLIT
发表于 2025-3-24 19:55:13
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Asparagus
发表于 2025-3-25 01:33:36
Intelligent Systems Reference Libraryhttp://image.papertrans.cn/r/image/822832.jpg