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• W2794833903 abstract "Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL^2), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset WebLogo-2M by an automatic web data collection and processing method. Extensive comparative evaluations demonstrate the superiority of the proposed SL^2 method over the state-of-the-art strongly and weakly supervised detection models and contemporary webly data learning approaches." @default.
• W2794833903 created "2018-04-06" @default.
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• W2794833903 date "2018-03-30" @default.
• W2794833903 modified "2023-10-17" @default.
• W2794833903 title "Scalable Deep Learning Logo Detection." @default.
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• W2794833903 hasPublicationYear "2018" @default.
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