  Contemporary domain adaptation offers a practical solution for achieving
cross-domain transfer of semantic segmentation between labeled source data and
unlabeled target data. These solutions have gained significant popularity;
however, they require the model to be retrained when the test environment
changes. This can result in unbearable costs in certain applications due to the
time-consuming training process and concerns regarding data privacy. One-shot
domain adaptation methods attempt to overcome these challenges by transferring
the pre-trained source model to the target domain using only one target data.
Despite this, the referring style transfer module still faces issues with
computation cost and over-fitting problems. To address this problem, we propose
a novel framework called Informative Data Mining (IDM) that enables efficient
one-shot domain adaptation for semantic segmentation. Specifically, IDM
provides an uncertainty-based selection criterion to identify the most
informative samples, which facilitates quick adaptation and reduces redundant
training. We then perform a model adaptation method using these selected
samples, which includes patch-wise mixing and prototype-based information
maximization to update the model. This approach effectively enhances adaptation
and mitigates the overfitting problem. In general, we provide empirical
evidence of the effectiveness and efficiency of IDM. Our approach outperforms
existing methods and achieves a new state-of-the-art one-shot performance of
56.7\%/55.4\% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively.
The code will be released at \url{https://github.com/yxiwang/IDM}.
