Recognition and perception-based mobile applications, such as image recognition, are on the rise. These applications recognize the users surroundings and augment it with information and/or media. These applications are latencysensitive. They have a soft-realtime nature - late results are potentially meaningless. On the one hand, given the computeintensive nature of the tasks performed by such applications, execution is typically offloaded to the cloud. On the other hand, offloading such applications to the cloud incurs network latency, which can increase the user-perceived latency. Consequently, edge-computing has been proposed to let devices offload intensive tasks to edge-servers instead of the cloud, to reduce latency. In this paper, we propose a different model for using edge-servers. We propose to use the edge as a specialized cache for recognition applications and formulate the expected latency for such a cache. We show that using an edge-server like a typical web-cache, for recognition applications, can lead to higher latencies. We propose Cachier, a system that uses the caching model along with novel optimizations to minimize latency by adaptively balancing load between the edge and the cloud by leveraging spatiotemporal locality of requests, using offline analysis of applications, and online estimates of network conditions. We evaluate Cachier for image-recognition applications and show that our techniques yield 3x speed-up in responsiveness, and perform accurately over a range of operating conditions. To the best of our knowledge, this is the first work that models edge-servers as caches for computeintensive recognition applications, and Cachier is the first system that uses this model to minimize latency for these applications.