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- W3169352167 abstract "Recent studies of driving scene understanding based on image semantic segmentation have achieved dramatic advances in speed and accuracy. Large-scale public datasets for semantic segmentation of paved road driving scenes have led the advances, but there is no large-scale public dataset for unpaved road environments. Building a large-scale image semantic segmentation dataset for unpaved roads is very expensive, and domain gaps between geographically distributed locations and those of seasonal changes hinder building a training dataset that is adequate to train a convolutional neural network model. In this paper, to resolve the data insufficiency problem, we use an one-shot learning setting in unpaved road driving scene understanding. Our One-shot Summary Prototypical Network (OSPNet) is trained with paved road driving scenes, and it identifies drivable regions in unpaved roads given only a single support image and unpaved road mask data. The OSPNet improves previous two branch few-shot segmentation approaches by introducing the summary branch which enables channel-wise weighting for important features in the feature map of support and query branches. Our experiments show that our model quantitatively and qualitatively outperforms recent supervised and few-shot segmentation models." @default.
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- W3169352167 date "2021-01-01" @default.
- W3169352167 modified "2023-10-17" @default.
- W3169352167 title "One-Shot Summary Prototypical Network Toward Accurate Unpaved Road Semantic Segmentation" @default.
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- W3169352167 doi "https://doi.org/10.1109/lsp.2021.3087457" @default.
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