Abstract: This talk describes hierarchical probabilistic models which have been applied to a range of vision problems including object detection, object parsing, and image segmentation and labeling. The design principle for these models is recursive composition so that the model are composed recursively from smaller models. We describe inference and learning algorithms for these models and show applications to benchmarked datasets. (Work with L. Zhu).