(2016) Weakly Supervised Object Boundaries.
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 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.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Conference: | CVPR IEEE Conference on Computer Vision and Pattern Recognition |
| Depositing User: | Sebastian Weisgerber |
| Date Deposited: | 22 Feb 2018 11:11 |
| Last Modified: | 22 Feb 2018 14:50 |
| URI: | https://publications.cispa.saarland/id/eprint/2418 |
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