UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. While UDA methods for synthetic to real-world domains (synth-to-real) show remarkable performance in tasks such as semantic segmentation and object detection, very few were proposed for instance segmentation in the field of vision-based autonomous driving, and the existing ones are based on a suboptimal baseline, which severely limits the performance. In this paper, we introduce UDA4Inst, a strong baseline of synth-to-real UDA for instance segmentation. UDA4Inst adopts cross-domain bidirectional data mixing at the instance level to effectively utilize data from both source and target domains. Rare-class balancing and category module training are also employed to further improve the performance. It is worth noting that we are the first to demonstrate results on two new synth-to-real instance segmentation benchmarks, with 39.0 mAP on UrbanSyn->Cityscapes and 35.7 mAP on Synscapes->Cityscapes. Our method outperforms the source-only Mask2Former model by +7 mAP and +7.6 mAP, respectively. On SYNTHIA->Cityscapes, our method improves the source-only Mask2Former by +6.7 mAP, achieving state-of-the-art results.Our code will be released soon.