The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks

Maxim Berman, Amal Rannen Triki, Matthew B. Blaschko
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
The Jaccard index, also referred to as the intersectionover-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance -which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses. We present 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
more » ... sz extension of submodular losses. The loss is shown to perform better with respect to the Jaccard index measure than the traditionally used cross-entropy loss. We show quantitative and qualitative differences between optimizing the Jaccard index per image versus optimizing the Jaccard index taken over an entire dataset. We evaluate the impact of our method in a semantic segmentation pipeline and show substantially improved intersection-over-union segmentation scores on the Pascal VOC and Cityscapes datasets using state-of-the-art deep learning segmentation architectures.
doi:10.1109/cvpr.2018.00464 dblp:conf/cvpr/BermanTB18 fatcat:t4trtsaxtvg33g6seavjasuat4