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Contemporary domain adaptation offers a practical solution for achievingcross-domain transfer of semantic segmentation between labeled source data andunlabeled target data. These solutions have gained significant popularity;however, they require the model to be retrained when the test environmentchanges. This can result in unbearable costs in certain applications due to thetime-consuming training process and concerns regarding data privacy. One-shotdomain adaptation methods attempt to overcome these challenges by transferringthe pre-trained source model to the target domain using only one target data.Despite this, the referring style transfer module still faces issues withcomputation cost and over-fitting problems. To address this problem, we proposea novel framework called Informative Data Mining (IDM) that enables efficientone-shot domain adaptation for semantic segmentation. Specifically, IDMprovides an uncertainty-based selection criterion to identify the mostinformative samples, which facilitates quick adaptation and reduces redundanttraining. We then perform a model adaptation method using these selectedsamples, which includes patch-wise mixing and prototype-based informationmaximization to update the model. This approach effectively enhances adaptationand mitigates the overfitting problem. In general, we provide empiricalevidence of the effectiveness and efficiency of IDM. Our approach outperformsexisting methods and achieves a new state-of-the-art one-shot performance of56.7\%/55.4\% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively.The code will be released at \url{https://github.com/yxiwang/IDM}.

Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation