Improving Semantic Segmentation via Video Propagation and Label Relaxation

Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks. We exploi...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 8848 - 8857
Hauptverfasser:	Zhu, Yi, Sapra, Karan, Reda, Fitsum A., Shih, Kevin J., Newsam, Shawn, Tao, Andrew, Catanzaro, Bryan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2019
Schlagworte:	Annotations Cognition Computer vision Deep Learning Grouping and Shape Image and Video Synthesis Predictive models Segmentation Semantics Training Uncertainty
ISSN:	1063-6919
Online-Zugang:	Volltext
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