Single-stage Semantic Segmentation from Image Labels
Beschreibung
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.
DFG-Fächer
4.43-04 Künstliche Intelligenz und Maschinelle Lernverfahren4.43-05 Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing
Verknüpfte Ressourcen
- Wird beschrieben durch: arXiv:2005.08104
Sammlungen
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Segmentation [4]
Die folgenden Lizenzbestimmungen sind mit dieser Ressource verbunden:
Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: Apache License 2.0