Object Detection Grammars – David McAllester (Toyota Technology Institute at Chicago)
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As statistical methods came to dominate computer vision, speech recognition and machine translation there was a tendency toward shallow models. The late Fred Jelinek is famously quoted as saying that every time he fired a linguist the performance of his speech recognition system improved. A major challenge of modern statistical methods is to demonstrate that deep models can be made to perform better than shallow models. This talk will describe an object detection system which tied for first place in the 2008 and 2009 PASCAL VOC object detection challenge and won a PASCAL “lifetime achievement” award in 2010. The system exploits a grammar model for representing object appearance. This model seems “deeper” than those used in the previous generation of statistically trained object detectors. This object detection system and the associated grammar formalism will be described in detail and future directions discussed.
Professor McAllester received his B.S., M.S., and Ph.D. degrees from the Massachusetts Institute of Technology in 1978, 1979, and 1987 respectively. He served on the faculty of Cornell University for the academic year of 1987-1988 and served on the faculty of MIT from 1988 to 1995. He was a member of technical staff at AT&T Labs-Research from 1995 to 2002. Since 2002 he has been Chief Academic Officer at the Toyota Technological Institute at Chicago. He has been a fellow of the American Association of Artificial Intelligence (AAAI) since 1997. A 1988 paper on computer game algorithms influenced the design of the algorithms used in the Deep Blue system that defeated Gary Kasparov. A 1991 paper on AI planning proved to be one of the most influential papers of the decade in that area. A 1998 paper on machine learning theory introduced PAC-Bayesian theorems which combine Bayesian and non-Bayesian methods. A 2001 paper with Andrew Appel introduced the influential step-index model of recursive types. He is currently part of a team that scored in the top two places in the PASCAL object detection challenge (computer vision) in 2007, 2008 and 2009.