Objects in a digital visual medium (i.e. image) are semantically segmented by determining (504) a score (i.e. probability) for each pixel representing the likelihood that the pixel corresponds to objects associated with bounding boxes (502) within the image. An instance-level label that yields a label for each pixel corresponding to the objects is determined (506) based, in part, on a collective probability map including each pixel score, e.g. using a CRF model. The score for each pixel corresponding to each bounding box may be determined by a neural network (NN) trained prediction model. Training may comprise receiving a training visual medium (i.e. image) having a first bounding box corresponding to a training target object and generating bounding boxes corresponding to the training target object based on the first box. The first and plurality of boxes together form a training set of boxes. Distance maps are generated, each corresponding to a respective box of the training set. The training image is concatenated with each map to generate a plurality of training pairs, and the NN is trained, based on at least one training pair of the plurality, to segment pixels of the training image corresponding to the training target object.
Instance-level semantic segmentation system