Single-Stage Semantic Segmentation from Image Labels
- URL: http://arxiv.org/abs/2005.08104v1
- Date: Sat, 16 May 2020 21:10:10 GMT
- Title: Single-Stage Semantic Segmentation from Image Labels
- Authors: Nikita Araslanov and Stefan Roth
- Abstract summary: This work defines three desirable properties of a weakly supervised method.
We then develop a segmentation-based network model and a self-supervised training scheme to train for semantic masks.
Despite its simplicity, our method achieves results that are competitive with significantly more complex pipelines.
- Score: 25.041129173350104
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent years have seen a rapid growth in new approaches improving the
accuracy of semantic segmentation in a weakly supervised setting, i.e. with
only image-level labels available for training. However, this has come at the
cost of increased model complexity and sophisticated multi-stage training
procedures. This is in contrast to earlier work that used only a single stage
$-$ training one segmentation network on image labels $-$ which was abandoned
due to inferior segmentation accuracy. In this work, we first define three
desirable properties of a weakly supervised method: local consistency, semantic
fidelity, and completeness. Using these properties as guidelines, we then
develop a segmentation-based network model and a self-supervised training
scheme to train for semantic masks from image-level annotations in a single
stage. We show that despite its simplicity, our method achieves results that
are competitive with significantly more complex pipelines, substantially
outperforming earlier single-stage methods.
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