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Conference Papers Year : 2014

On learning to localize objects with minimal supervision

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Abstract

Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level labels of whether the objects are present or not. Our approach combines a discriminative submodular cover problem for automatically discovering a set of positive object windows with a smoothed latent SVM formulation. The latter allows us to leverage efficient quasi Newton optimization techniques. Our experiments demonstrate that the proposed approach provides a 50% relative improvement in mean average precision over the current state-of-the-art on PASCAL VOC 2007 detection.
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Dates and versions

hal-00996849 , version 1 (27-05-2014)

Identifiers

  • HAL Id : hal-00996849 , version 1

Cite

Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, et al.. On learning to localize objects with minimal supervision. ICML - 31st International Conference on Machine Learning, Jun 2014, Beijing, China. pp.1611-1619. ⟨hal-00996849⟩
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