COUPLED MULTI-TASK FULLY CONVOLUTIONAL NETWORKS USING MULTI-SCALE CONTEXTUAL INFORMATION AND HIERARCHICAL HYPER-FEATURES FOR SEMANTIC IMAGE SEGMENTATION

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.