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COUPLED MULTI-TASK FULLY CONVOLUTIONAL NETWORKS USING MULTI-SCALE CONTEXTUAL INFORMATION AND HIERARCHICAL HYPER-FEATURES FOR SEMANTIC IMAGE SEGMENTATION
COUPLED MULTI-TASK FULLY CONVOLUTIONAL NETWORKS USING MULTI-SCALE CONTEXTUAL INFORMATION AND HIERARCHICAL HYPER-FEATURES FOR SEMANTIC IMAGE SEGMENTATION
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机译:使用多尺度上下文信息和分层超特征进行语义图像分割的多任务完全卷积网络
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摘要
Techniques related to implementing fully convolutional networks for semantic image segmentation are discussed. Such techniques may include combining feature maps from multiple stages of a multi-stage fully convolutional network to generate a hyper-feature corresponding to an input image, up-sampling the hyper-feature and summing it with a feature map of a previous stage to provide a final set of features, and classifying the final set of features to provide semantic image segmentation of the input image.
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