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- W1657775314 abstract "Learning to localize objects with minimal supervision is an important problemin computer vision, since large fully annotated datasets are extremely costlyto obtain. In this paper, we propose a new method that achieves this goal withonly image-level labels of whether the objects are present or not. Our approachcombines a discriminative submodular cover problem for automaticallydiscovering a set of positive object windows with a smoothed latent SVMformulation. The latter allows us to leverage efficient Quasi-Newtonoptimization techniques. Our experiments demonstrate that the proposed approachprovides approximately 70% relative improvement in average precision over thecurrent state of the art on standard benchmark datasets." @default.
- W1657775314 created "2016-06-24" @default.
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- W1657775314 date "2014-03-05" @default.
- W1657775314 modified "2023-09-24" @default.
- W1657775314 title "One-Bit Object Detection: On learning to localize objects with minimal supervision." @default.
- W1657775314 hasPublicationYear "2014" @default.
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