RAFT - A Domain Adaptation Framework for RGB & LiDAR Semantic Segmentation

The introduction of the RAFT framework marks a significant advancement in the field of image segmentation, particularly in addressing the challenges posed by the Syn2Real problem. This framework allows for the adaptation of segmentation models using minimal labeled real-world data, which is crucial given the high costs associated with manual data collection and annotation. In rigorous testing against established benchmarks, RAFT demonstrated notable improvements, achieving a 2.1% increase in mean Intersection over Union (mIoU) for the SYNTHIA->Cityscapes benchmark and a 0.4% increase for GTAV->Cityscapes. Furthermore, it surpassed the previous state-of-the-art model, HALO, in these evaluations, showcasing its effectiveness. The framework's ability to also improve performance in real-to-real scenarios, such as Cityscapes->ACDC with a 1.3% gain, underscores its versatility and potential for real-world applications, making it a valuable tool for advancing semantic segmentation in various …