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Colloquium: Understanding Visual Appearances in the Long-Tail

Date: 

Thursday, October 1, 2015 - 11:00am

Speaker: 

Deva Ramanan

Host: 

Ryan Farrell

Understanding Visual Appearances in the Long-Tail
Thursday, October 1, 2015
11 am in 1170 TMCB

Abstract:
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.
 

Biography: 

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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