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The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks
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
doi:10.1109/cvpr.2018.00464
dblp:conf/cvpr/BermanTB18
fatcat:t4trtsaxtvg33g6seavjasuat4