Learning to Adapt Structured Output Space for Semantic Segmentation

Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to t...

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Vydané v:	2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition s. 7472 - 7481
Hlavní autori:	Tsai, Yi-Hsuan, Hung, Wei-Chih, Schulter, Samuel, Sohn, Kihyuk, Yang, Ming-Hsuan, Chandraker, Manmohan
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2018
Predmet:	Adaptation models Image segmentation Layout Prediction algorithms Semantics Task analysis Training
ISSN:	1063-6919
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