Clark Hall 314
Challenges in Scaling Up Visual Recognition to the Open World
Visual recognition is currently undergoing a period of rapid progress.nThis talk will examine challenges in scaling up current approaches, which are often focused on curated “closed world” datasets and recognition tasks, to the “open-world” world, which typically contains massive numbers ofunbalanced classes, uncontrolled factors of variation, and never-before-seen tasks of interest. Challenges in data distribution modeling and computational efficiency will be addressed, with a focus on the illustrative usecase of streaming video analysis.
Deva Ramanan is an associate professor at the Robotics Institute at Carnegie- Mellon University. Prior to joining CMU, he was an associate professor at UC Irvine. His research interests span computer vision and machine learning, with a focus on visual recognition. He was awarded the David Marr Prize in 2009, the PASCAL VOC Lifetime Achievement Prize in 2010, an NSF Career Award in 2010, the UCI Chancellor’s Award for Excellence in Undergraduate Research in 2011, the PAMI YoungResearcher Award in 2012, and was selected as one of Popular Science’s Brilliant 10 researchers in 2012. His work is supported by NSF, ONR, DARPA, as well as industrial collaborations with Intel, Google, and Microsoft.