Weakly Supervised Object Boundaries

Anna Khoreva, Rodrigo Benenson, Mohamed Omran, Matthias Hein, Bernt Schiele; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 183-192


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.

Related Material

author = {Khoreva, Anna and Benenson, Rodrigo and Omran, Mohamed and Hein, Matthias and Schiele, Bernt},
title = {Weakly Supervised Object Boundaries},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}