Training a neural network to predict superpixels using segmentation-aware affinity loss

A system, comprising:

• a loss module configured to:
• receive input data corresponding to pixels in an image;
• receive ground-truth object segmentation data corresponding to the input data, wherein the ground-truth object segmentation data identifies objects in the image and the pixels within each object;
• and
• process the ground-truth object segmentation data and a superpixel map to compute correction data, the superpixel map corresponding to the input data and indicating regions of the pixels within each object;
• and
• a pixel affinity neural network model configured to process the input data using parameters to produce predicted pixel affinity values corresponding to the image, wherein the parameters are updated based on the correction data.

A non-transitory, computer-readable storage medium storing instructions that, when executed by a processing unit, cause the processing unit to:

• receive input data corresponding to pixels in an image;
• receive ground-truth object segmentation data corresponding to the input data, wherein the ground-truth object segmentation data identifies objects in the image and the pixels within each object;
• process the ground-truth object segmentation data and a superpixel map to compute correction data, the superpixel map corresponding to the input data and indicating regions of the pixels within each object;
• and
• process the input data using parameters to produce predicted pixel affinity values corresponding to the image, wherein the parameters are updated based on the correction data.