Event Location: NSH 1507
Bio: Jacob Walker is a Ph.D. student at the Robotics Institute, Carnegie Mellon University, where he is supervised by Abhinav Gupta and Martial Hebert. He holds a B.S. in Computer Science, a B.S. in Mathematics, and a B.S. in Economics from Michigan State University. His current research focuses on predicting dynamic events in static images.

Abstract: This is a joint work with Abhinav Gupta and Martial Hebert. In this work we present a conceptually simple but surprisingly powerful method for visual prediction which combines the effectiveness of mid-level visual elements with temporal modeling. Our framework can be learned in a completely unsupervised manner from a large collection of videos. However, more importantly, because our approach models the prediction framework on these mid-level elements, we can not only predict the possible motion in the scene but also predict visual appearances — how are appearances going to change with time. This yields a visual “hallucination” of probable events on top of the scene. We show that our method is able to accurately predict and visualize simple future events; we also show that our approach is comparable to supervised methods for event prediction