Style Adaptation for Domain-adaptive Semantic Segmentation

arXiv:2404.16301v1 Announce Type: new
Abstract: Unsupervised Domain Adaptation (UDA) refers to the method that utilizes annotated source domain data and unlabeled target domain data to train a model capable of generalizing to the target domain data. Domain discrepancy leads to a significant decrease in the performance of general network models trained on the source domain data when applied to the target domain. We introduce a straightforward approach to mitigate the domain discrepancy, which necessitates no additional parameter calculations and seamlessly …

abstract arxiv cs.cv data domain domain adaptation general leads network performance segmentation semantic style train type unsupervised