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- W2034603029 abstract "We argue that object subcategories follow a long-tail distribution: a few subcategories are common, while many are rare. We describe distributed algorithms for learning large- mixture models that capture long-tail distributions, which are hard to model with current approaches. We introduce a generalized notion of mixtures (or subcategories) that allow for examples to be shared across multiple subcategories. We optimize our models with a discriminative clustering algorithm that searches over mixtures in a distributed, brute-force fashion. We used our scalable system to train tens of thousands of deformable mixtures for VOC objects. We demonstrate significant performance improvements, particularly for object classes that are characterized by large appearance variation." @default.
- W2034603029 created "2016-06-24" @default.
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- W2034603029 date "2014-06-01" @default.
- W2034603029 modified "2023-09-30" @default.
- W2034603029 title "Capturing Long-Tail Distributions of Object Subcategories" @default.
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- W2034603029 doi "https://doi.org/10.1109/cvpr.2014.122" @default.
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