UTCS AI Colloquia - Charless Fowlkes, Associate Professor, UC Irvine, "How can object detectors exploit growing quantities of training data?"
Talk Audience: UTCS Faculty and Graduate Students
Host: Kristen Grauman
Talk Abstract: A natural question for computer vision is whether accuracy of existing systems for object detection is limited by the amount of available training data or by the features and learning algorithms used to train them. I'll describe a series of experiments we carried out to understand how discriminatively trained template-based detectors perform as we increase both the amount of positive training examples and the model complexity. These results suggest some new ways in which template-based detectors can be grown non-parametrically to take better advantage of data. I will also present some recent results on using synthetically generated data in order to learn appearance models for partially occluded people.
Speaker Bio: Charless Fowlkes is an Associate Professor in the Dept. of Computer Science at the University of California, Irvine. His research interests are in computational vision and applications to biological image analysis. Prior to joining UCI he received a PhD from UC Berkeley in 2005 a BS from Caltech in 2000. He is a recipient of an NSF CAREER award and a Marr best-paper prize.
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