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Lecture Notes in Computer Science
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an imageto-image mapping. While these methods effectively exploit global image context, the learning and computational complexities are high. We propose shared memory augmented neural network actors as a dynamically scalable alternative. Based on a decomposition of the image into a sequence of local patches, we train such actors to sequentiallydoi:10.1007/978-3-030-20351-1_65 fatcat:ljh3qhdvwbemngr77nkidmbqoq