Tomorrow, Jonathan Huang and I are giving a Computer Vision tutorial at the First MLD (Machine Learning Department) Research Symposium at CMU. The title of our presentation is Learning and Inference in Vision: from Features to Scene Understanding.
The goal of the tutorial is to expose Machine Learning students to state-of-the-art object recognition, scene understanding and the inference problems associated with such high-level recognition problems. Our target audience is graduate students with little or no prior exposure to object recognition who would like to learn more about the use of probabilistic graphical models in Computer Vision. We outline the difficulties present in object recognition/detection and outline several different models for jointly reasoning about multiple object hypotheses.