A computer-implemented method comprising: providing an image as input to a descriptor neural network that processes the image to generate outputs including: (i) data identifying one or more regions of the image, wherein an object is depicted in each region, (ii) for each region, a predicted type of object that is depicted in the region, and (iii) feature channels comprising a plurality of semantic channels and one or more direction channels, wherein: each semantic channel is associated with a particular type of object and defines, for each pixel of the image, a respective likelihood that the pixel is included in an object of the particular type; and the direction channels characterize a predicted direction from each pixel of the image to a center of an object depicted in the image which includes the pixel; generating a region descriptor for each of the one or more regions, including, for each region: for a plurality of feature channels, comprising at least one direction channel, extracting feature data corresponding to the region of the image from the feature channels; resizing the extracted feature data to a pre-determined dimensionality; and concatenating the resized feature data; and providing the region descriptor for each of the one or more regions to a segmentation neural network that processes a region descriptor for a region to generate an output comprising a predicted segmentation of the predicted type of object depicted in the region, wherein generating a region descriptor for a region further comprises extracting feature data corresponding to the region of the image from the semantic feature channel associated with the predicted type of object depicted in the region. providing an image as input to a descriptor neural network that processes the image to generate outputs including: (i) data identifying one or more regions of the image, wherein an object is depicted in each region, (ii) for each region, a predicted type of object that is depicted in the region, and (iii) feature channels comprising a plurality of semantic channels and one or more direction channels, wherein: each semantic channel is associated with a particular type of object and defines, for each pixel of the image, a respective likelihood that the pixel is included in an object of the particular type; and the direction channels characterize a predicted direction from each pixel of the image to a center of an object depicted in the image which includes the pixel; each semantic channel is associated with a particular type of object and defines, for each pixel of the image, a respective likelihood that the pixel is included in an object of the particular type; and the direction channels characterize a predicted direction from each pixel of the image to a center of an object depicted in the image which includes the pixel; generating a region descriptor for each of the one or more regions, including, for each region: for a plurality of feature channels, comprising at least one direction channel, extracting feature data corresponding to the region of the image from the feature channels; resizing the extracted feature data to a pre-determined dimensionality; and concatenating the resized feature data; and for a plurality of feature channels, comprising at least one direction channel, extracting feature data corresponding to the region of the image from the feature channels; resizing the extracted feature data to a pre-determined dimensionality; and concatenating the resized feature data; and providing the region descriptor for each of the one or more regions to a segmentation neural network that processes a region descriptor for a region to generate an output comprising a predicted segmentation of the predicted type of object depicted in the region, wherein generating a region descriptor for a region further comprises extracting feature data corresponding to the region of the image from the semantic feature channel associated with the predicted type of object depicted in the region.