Understanding visual appearances in the long-tail - DLS Talk by Deva Ramanan, CMU


DMP 110

Hugh Dempster Building (6245 Agronomy Rd.), Room 110

Speaker:  Deva Ramanan, Associate Professor, Carnegie Mellon University

Title:  Understanding Visual Appearances in the Long-tail

Host:  Jim Little, UBC Computer Science


Computer vision is currently undergoing a period of rapid progress, brought in part through the integration of machine-learning techniques with big training datasets. This talk will attempt to examine some of the modeling insights behind this progress, as well as open challenges that remain. A well-known but under-appreciated observation is that visual phenomena follows a long-tail distribution: a few modes of appearance are common, while many rare modes are in the tail. As an example, people commonly stand or walk, but can contort their body into many more poses. I will argue that the 'tail' remains the open challenge because training data is limited (even in the big-data setting). I will describe some promising methods that address this difficulty by synthesizing new data examples, either explicitly with a computer graphics pipeline or implicitly through compositional representations. The latter view suggests novel variants of deep architectures that reason about compositional variables. I will conclude by demonstrating such architectures on various visual recognition tasks, including perceptual grouping, object recognition, and people tracking.  


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 Young Researcher 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 the Intel, Google, and Microsoft.

He is on the editorial board of the International Journal of Computer Vision (IJCV) and is an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). He regularly serves as a senior program committee member for the IEEE Conference of Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV), and the European Conference on Computer Vision (ECCV). He also regularly serves on NSF panels for computer vision and machine learning.

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