We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image.Although similar problems have been studied in few-shot object counting and detection (FSCD), previous methods and their benchmarks have narrowed patterns of interest to object categories and often fail to localize non-object patterns. In this work, we propose a simple yet effective detector based on template matching and regression, dubbed \ours.While previous FSCD methods typically represent given target exemplars into a spatially collapsed prototype, we revisit classic template matching and regression. It effectively preserves and leverages the spatial layout of exemplars in our minimalistic architecture, which consists of a few learnable layers of either convolutions or projections.We also introduce a new dataset, dubbed RPINE, which covers a wider range of patterns than existing object-centric datasets.Experiments on three benchmarks, RPINE, FSCD-147, FSCD-LVIS, demonstrate that our method outperforms recent state-of-the-art methods, showing an outstanding generalization ability on cross-dataset evaluation.