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• W2026124029 abstract "Current similarity-based approaches to interactive fine grained categorization rely on learning metrics from holistic perceptual measurements of similarity between objects or images. However, making a single judgment of similarity at the object level can be a difficult or overwhelming task for the human user to perform. Secondly, a single general metric of similarity may not be able to adequately capture the minute differences that discriminate fine-grained categories. In this work, we propose a novel approach to interactive categorization that leverages multiple perceptual similarity metrics learned from localized and roughly aligned regions across images, reporting state-of-the-art results and outperforming methods that use a single nonlocalized similarity metric." @default.
• W2026124029 created "2016-06-24" @default.
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• W2026124029 date "2015-01-01" @default.
• W2026124029 modified "2023-09-27" @default.
• W2026124029 title "Learning Localized Perceptual Similarity Metrics for Interactive Categorization" @default.
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• W2026124029 doi "https://doi.org/10.1109/wacv.2015.73" @default.
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