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Birdsnap: Large-scale Fine-grained Visual Categorization of Birds
We show how these priors can be used to significantly improve performance.
POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation
From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs-One Features (POOFs).
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