We consider the problem of crowd counting in static images. Given an image, the goal is to estimate a density map of this image, where each value in the density map indicates the density level of the corresponding location in the image. In particular, we consider a novel problem setting which we call the one-shot scene-specific crowd counting. During training, we assume that we have labeled images collected from different scenes. Each scene corresponds to a camera at a fixed location and angle. Given a target scene, we assume that we have one single labeled image collected from that scene. Our goal is to adapt the crowd counting model to this specific scene based on this single example. We argue that this setting is more realistic in terms of deploying crowd counting algorithms in real-world applications. We propose a novel one-shot learning approach for learning how to adapt to a target scene using one labeled example. Our experiment results demonstrate that our proposed approach outperforms other alternative methods.