  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.
