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System and Method for Learning Random-Walk Label Propagation for Weakly-Supervised Semantic Segmentation
System and Method for Learning Random-Walk Label Propagation for Weakly-Supervised Semantic Segmentation
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机译:学习用于弱监督语义分割的随机步行标签传播的系统和方法
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摘要
Systems and methods for training semantic segmentation. Embodiments of the present invention include predicting semantic labeling of each pixel in each of at least one training image using a semantic segmentation model. Further included is predicting semantic boundaries at boundary pixels of objects in the at least one training image using a semantic boundary model concurrently with predicting the semantic labeling. Also included is propagating sparse labels to every pixel in the at least one training image using the predicted semantic boundaries. Additionally, the embodiments include optimizing a loss function according the predicted semantic labeling and the propagated sparse labels to concurrently train the semantic segmentation model and the semantic boundary model to accurately and efficiently generate a learned semantic segmentation model from sparsely annotated training images.
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