Road layout randomization on semantic labels produces synthetic training pairs that improve mIoU for rare road marking classes by over 12 percentage points in real-world urban deployment while retaining performance on other classes.
Pixel level data augmentation for semantic image segmentation using generative adversarial networks,
2 Pith papers cite this work. Polarity classification is still indexing.
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Two data selection techniques (GMM visual similarity and bounding-box diversity) reduce required weakly labeled images by up to 100x on Open Images and 20x on Cityscapes while maintaining semantic segmentation performance.
citing papers explorer
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Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
Road layout randomization on semantic labels produces synthetic training pairs that improve mIoU for rare road marking classes by over 12 percentage points in real-world urban deployment while retaining performance on other classes.
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Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
Two data selection techniques (GMM visual similarity and bounding-box diversity) reduce required weakly labeled images by up to 100x on Open Images and 20x on Cityscapes while maintaining semantic segmentation performance.