Graphical models provide a powerful framework for expressing and solving a variety of inference problems. The approach has had an enormous impact in computer vision. In this talk I will review some of the developments that have enabled this impact, focusing on efficient algorithms that exploit the structure of vision problems. I will discuss several applications including the low-level vision problem of image restoration, the mid-level problem of segmentation and the high-level problem of model-based recognition. I will also discuss some of the current challenges in the area.