The Lov´asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

This work presents a method for direct optimization of the mean intersection-over-union loss in neural networks, in the context of semantic image segmentation, based on the convex Lov´asz extension of sub-modular losses.