14. A computing system for segmenting an image based on an n-gram that references a first region of the image, wherein the image includes a plurality of pixels and the n-gram includes an ordered set of tokens, the system comprising: a processor device; and a computer-readable storage medium, coupled with the processor device, having instructions stored thereon, which, when executed by the processor device, perform actions comprising: steps for identifying a plurality of images features within the image based on an image feature identification model; steps for encoding a mapping between each of the plurality of image features and a corresponding portion of the plurality of pixels in an image data structure; steps for identifying semantic features for each token in the set of tokens based on a natural language model; steps for encoding the sematic features of each token in the set of tokens as a set of token data structures; steps for iteratively updating a segmentation map based on the segmentation map and an ordered set of combinations of the image data structure and the set of token data structures based on an order of the set of tokens; and steps for providing a segmented image based on the image and the segmentation map.
14. A computing system for segmenting an image based on an n-gram that references a first region of the image, wherein the image includes a plurality of pixels and the n-gram includes an ordered set of tokens, the system comprising: a processor device; and a computer-readable storage medium, coupled with the processor device, having instructions stored thereon, which, when executed by the processor device, perform actions comprising: steps for identifying a plurality of images features within the image based on an image feature identification model; steps for encoding a mapping between each of the plurality of image features and a corresponding portion of the plurality of pixels in an image data structure; steps for identifying semantic features for each token in the set of tokens based on a natural language model; steps for encoding the sematic features of each token in the set of tokens as a set of token data structures; steps for iteratively updating a segmentation map based on the segmentation map and an ordered set of combinations of the image data structure and the set of token data structures based on an order of the set of tokens; and steps for providing a segmented image based on the image and the segmentation map. 19. The computing system of claim 14 , wherein the image feature identification model is implemented on a trained fully convolutional neural network (FCN).