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Weakly Supervised Object Boundaries
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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
doi:10.1109/cvpr.2016.27
dblp:conf/cvpr/KhorevaBO0S16
fatcat:vbyolr7dqfet7geuut7zmvceye