Weakly Supervised Object Boundaries

Anna Khoreva, Rodrigo Benenson, Mohamed Omran,

Matthias Hein and Bernt Schiele

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 withoutusing 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.

 

 

Data

 

For further information or data, please contact Anna Khoreva <khoreva at mpi-inf.mpg.de>.

References

[Khoreva et al., 2016] Weakly Supervised Object Boundaries, A. Khoreva, R. Benenson, M. Omran,  M. Hein and B. Schiele, Computer Vision and Pattern Recognition (CVPR), June, (2016), (spotlight)

 

 

@inproceedings{khoreva16cvpr,
title={Weakly Supervised Object Boundaries
},
author={A. Khoreva and R. Benenson and M. Omran and M. Hein and B. Schiele},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016}}