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Weakly Supervised Object Boundaries

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons79309

Khoreva,  Anna
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons79212

Benenson,  Rodrigo
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons136609

Omran,  Mohamed
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Zitation

Khoreva, A., Benenson, R., Omran, M., Hein, M., & Schiele, B. (2016). Weakly Supervised Object Boundaries. In 29th IEEE Conference on Computer Vision and Pattern Recognition (pp. 183-192). Los Alamitos, CA: IEEE Computer Society. doi:10.1109/CVPR.2016.27.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002B-2645-2
Zusammenfassung
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.