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.　11Code　available　at:　https://github.com/siyandong/SRC　©　2022　IEEE.