  We tackle the problem of visual localization under changing conditions, such
as time of day, weather, and seasons. Recent learned local features based on
deep neural networks have shown superior performance over classical
hand-crafted local features. However, in a real-world scenario, there often
exists a large domain gap between training and target images, which can
significantly degrade the localization accuracy. While existing methods utilize
a large amount of data to tackle the problem, we present a novel and practical
approach, where only a few examples are needed to reduce the domain gap. In
particular, we propose a few-shot domain adaptation framework for learned local
features that deals with varying conditions in visual localization. The
experimental results demonstrate the superior performance over baselines, while
using a scarce number of training examples from the target domain.
