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arXiv · 1511.07803

Weakly Supervised Object Boundaries

Anna Khoreva·Rodrigo Benenson·Mohamed Omran·Matthias Hein·Bernt Schiele
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Abstract

State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-the-art methods.

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Anna Khoreva, Rodrigo Benenson, Mohamed Omran, Matthias Hein, Bernt Schiele. 2015-11-24. Weakly Supervised Object Boundaries. https://arxiv.org/abs/1511.07803

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