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