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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Bibliographic Details

Published in:	2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 7472 - 7481
Main Authors:	Tsai, Yi-Hsuan, Hung, Wei-Chih, Schulter, Samuel, Sohn, Kihyuk, Yang, Ming-Hsuan, Chandraker, Manmohan
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2018
Subjects:	Adaptation models Image segmentation Layout Prediction algorithms Semantics Task analysis Training
ISSN:	1063-6919
Online Access:	Get full text
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