Structured Prediction Models for High-level Computer Vision Tasks

September 28, 2011
Sebastian Nowozin | Microsoft

Rich statistical models have revolutionized computer vision research:

graphical models and structured prediction in particular are now commonly used
tools to address hard computer vision problems. I discuss what distinguishes
these computer vision problems from other machine learning problems and how
this poses unique challenges.

I argue that most computer vision models are misspecified and discuss the
consequences of popular estimators in this case, concluding that we either
have to use non-parametric models or use estimators robust to
misspecification.

As one possible solution, I propose a novel discrete random field model
applicable to a large number of computer vision tasks. The model is
conditionally specified, non-parametric, and able to represent complex label
interactions, yet it can be trained from hundreds of images in minutes on a
single machine.

Speaker Details

Sebastian Nowozin is a researcher in the Machine Learning and Perception group
at Microsoft Research Cambridge. He received his Master of Engineering degree
from the Shanghai Jiaotong University and his diploma degree in computer
science with distinction from the Technical University of Berlin in 2006. He
received his PhD degree summa cum laude in 2009 for his thesis on learning
with structured data in computer vision, completed at the Max Planck Institute
for Biological Cybernetics, Tuebingen and the Technical University of Berlin.
His research interest is diverse and includes computer vision, machine
learning, and continuous and discrete optimization. He organizes the
successful “Optimization for Machine Learning” workshop series at NIPS (OPT
2008-2011) and serves as PC-member/reviewer for machine learning (e.g. NIPS,
ICML, AISTATS, UAI, ECML, JMLR) and computer vision (e.g. CVPR, ICCV, ECCV,
PAMI, IJCV) conferences/journals.