ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes

Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Synthetic-to-Real Translation GTAV-to-Cityscapes Labels ROAD mIoU 39.4 # 22

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