  Visual (re)localization addresses the problem of estimating the 6-DoF (Degree
of Freedom) camera pose of a query image captured in a known scene, which is a
key building block of many computer vision and robotics applications. Recent
advances in structure-based localization solve this problem by memorizing the
mapping from image pixels to scene coordinates with neural networks to build
2D-3D correspondences for camera pose optimization. However, such memorization
requires training by amounts of posed images in each scene, which is heavy and
inefficient. On the contrary, few-shot images are usually sufficient to cover
the main regions of a scene for a human operator to perform visual
localization. In this paper, we propose a scene region classification approach
to achieve fast and effective scene memorization with few-shot images. Our
insight is leveraging a) pre-learned feature extractor, b) scene region
classifier, and c) meta-learning strategy to accelerate training while
mitigating overfitting. We evaluate our method on both indoor and outdoor
benchmarks. The experiments validate the effectiveness of our method in the
few-shot setting, and the training time is significantly reduced to only a few
minutes. Code available at: \url{https://github.com/siyandong/SRC}
