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• W3121898985 abstract "Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor- and time-consuming to produce. Moreover, professional photo interpreters might have to be involved for guaranteeing the correctness of annotations. To alleviate such a burden, we propose a framework for semantic segmentation of aerial images based on incomplete annotations, where annotators are asked to label a few pixels with easy-to-draw scribbles. To exploit these sparse scribbled annotations, we propose the FEature and Spatial relaTional regulArization (FESTA) method to complement the supervised task with an unsupervised learning signal that accounts for neighbourhood structures both in spatial and feature terms." @default.
• W3121898985 created "2021-02-01" @default.
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• W3121898985 date "2022-01-01" @default.
• W3121898985 modified "2023-10-01" @default.
• W3121898985 title "Semantic Segmentation of Remote Sensing Images With Sparse Annotations" @default.
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• W3121898985 doi "https://doi.org/10.1109/lgrs.2021.3051053" @default.
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