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

The Jaccard index, also referred to as the intersection-over-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 Lov\'asz extension of submodular losses... (read more)

PDF Abstract

Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Real-Time Semantic Segmentation Cityscapes test ENet + Lovász-Softmax mIoU 63.1% # 19
Time (ms) 13 # 5
Frame (fps) 76.9 # 4
Semantic Segmentation Cityscapes test ENet + Lovász-Softmax Mean IoU (class) 63.06% # 64
Semantic Segmentation PASCAL VOC 2012 test Deeplab-v2 + Lovász-Softmax Mean IoU 79.0% # 29
Semantic Segmentation PASCAL VOC 2012 test Deeplab-v2 with Lovasz-Softmax loss Mean IoU 79.00% # 29

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet