Date & Time:
Thursday, May 23, 2013; 12:00 PM
The development of autonomous systems that can effectively assist people with everyday tasks is one of the grand challenges in modern computer science. Notable examples are personal robotics for the elderly and people with disabilities, as well as autonomous driving systems which can help decrease fatalities caused by traffic accidents. To achieve full autonomy, multiple perception tasks must be solved: Autonomous systems should sense the environment, recognize the 3D world and interact with it. While most approaches have tackled individual perceptual components in isolation, I believe that the next generation of perceptual systems should reason jointly about multiple tasks.
In this talk I'll argue that there are four key aspects towards developing such holistic models: (i) learning, (ii) inference (iii) representation, and (iv) data. I'll describe efficient Markov random field learning and inference algorithms that exploit both the structure of the problem as well as parallel computation to achieve computational and memory efficiency. I'll demonstrate the effectiveness of our models on a wide variety of examples, and show representations and inference strategies that allow us to achieve state-of-the-art performance and result in several orders of magnitude speed-ups in a variety of challenging tasks, including 3D reconstruction, 3D layout parsing, object detection, semantic segmentation and free text exploitation for holistic visual recognition.
Prof. Raquel Urtasun
Raquel Urtasun is an Assistant Professor at TTI-Chicago, a philanthropically endowed academic institute located in the campus of the University of Chicago. She was a visiting professor at ETH Zurich during the spring semester of 2010. Previously, she was a postdoctoral research scientist at UC Berkeley and ICSI and a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Raquel Urtasun completed her PhD at the Computer Vision Laboratory, at EPFL, Switzerland in 2006 working with Pascal Fua and David Fleet. She has been area chair of multiple learning and vision conferences (i.e., NIPS, UAI, ICML, ICCV), she is an editor of the International Journal of Computer Vision (IJCV), and has served in the committee of numerous international conferences in computer vision, machine learning and computer graphics. Her major interests are statistical machine learning and computer vision, with a particular interest in non-parametric Bayesian statistics, latent variable models, structured prediction and their application to scene understanding.