LearningfromFuture: ANovelSelf-Training FrameworkforSemanticSegmentation-SupplementaryMaterial-YeDu1,2 YujunShen3 HaochenWang4 JingjingFei5 WeiLi5 LiweiWu5 RuiZhao5,6 ZehuaFu1,2 QingjieLiu1,2

C provide more ablation studies of our FST, including the ablation on SYNTHIA Cityscapes and evaluation of various segmentation decoders. PASCALVOC2012 [6] consists of21 classes with1,464, 1,449, and 1,456 images for the training, validation, and test set,respectively. Ablation on SYNTHIA.We also provide ablation results on SYNTHIA Cityscapes UDA benchmark andtheresults areshowninTab.S2. The MLP head fuses multi-levelfeatures and upsamples the feature map to predict the segmentation mask, which is designed for Transformer-based segmentation model[36]. We compare our FST with previous state-of-the-art semi-supervised semantic segmentation frameworks, including CCT [22], GCT [16]and CPS [3].