Recognition by association via learning per-exemplar distances

Abstract

We pose the recognition problem as data association. In this setting, a novel object is explained solely in terms of a small set of exemplar objects to which it is visually similar. Inspired by the work of Frome et al., we learn separate distance functions for each exemplar; however, our distances are interpretable on an absolute scale and can be… (More)
DOI: 10.1109/CVPR.2008.4587462

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