A computer-implemented method for semantic segmentation of an image determines unary energy of each pixel in an image using a first subnetwork, determines pairwise energy of at least some pairs of pixels of the image using a second subnetwork, and determines, using a third subnetwork, an inference on a Gaussian random field (GRF) minimizing an energy function including a combination of the unary energy and the pairwise energy. The GRF inference defining probabilities of semantic labels for each pixel in the image, and the method converts the image into a semantically segmented image by assigning to a pixel in the semantically segmented image a semantic label having the highest probability for a corresponding pixel in the image among the probabilities determined by the third subnetwork. The first subnetwork, the second subnetwork, and the third subnetwork are parts of a neural network.
SYSTEM AND COMPUTER-IMPLEMENTED METHOD FOR SEMANTIC SEGMENTATION OF IMAGE, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM