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This report describes the winning solution to the Robust Vision Challenge(RVC) semantic segmentation track at ECCV 2022. Our method adopts theFAN-B-Hybrid model as the encoder and uses SegFormer as the segmentationframework. The model is trained on a composite dataset consisting of imagesfrom 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash2, IDD, BDD, and COCO) with a simple dataset balancing strategy. All theoriginal labels are projected to a 256-class unified label space, and the modelis trained using a cross-entropy loss. Without significant hyperparametertuning or any specific loss weighting, our solution ranks the first place onall the testing semantic segmentation benchmarks from multiple domains (ADE20K,Cityscapes, Mapillary Vistas, ScanNet, VIPER, and WildDash 2). The proposedmethod can serve as a strong baseline for the multi-domain segmentation taskand benefit future works. Code will be available athttps://github.com/lambert-x/RVC_Segmentation.

1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track