[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Philipp Kräehenbüehl, Monday April 4, 12:30pm

Barbara Engelhardt bee at princeton.edu
Thu Mar 31 09:42:47 EDT 2016

Talk of interest on Monday.

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Colloquium Speaker
Philipp Kräehenbüehl, University of California, Berkeley
Monday, April 4, 2016 - 12:30pm
Computer Science 105

The many ways to understand the pixels

The field of computer vision is arguably seeing one of its most
transformative changes in recent history. Convolutional neural networks
(CNNs) have revolutionized the field, reaching super-human performance on
some long-standing computer vision tasks, such as image classification. The
success of these networks is fueled by massive amounts of human-labeled
data. However this paradigm does not scale to a deeper and more detailed
understanding of images, as it is simply too hard to collect enough
human-labeled data. The issue is not that we humans don't understand the
image, but we often struggle to convey enough information to successfully
supervise a vision system.

In this talk I show how computer vision can go beyond massive human
supervision. This involves designing better models that deal with fewer
labels, exploiting easier and more intuitive annotations, or coming up with
novel optimizations to train deep architectures with far fewer human
annotations, or even without any at all. I'll focus on three long standing
computer vision problems: semantic segmentation, intrinsic image
decomposition and dense semantic correspondences.

Philipp Krähenbühl is a postdoctoral researcher at UC Berkeley. He received
a B.S. in Computer Science from ETH Zurich in 2009, and a PhD in Computer
Science from Stanford University in 2014. Philipp's research interests lie
in Computer vision, Machine learning and Computer Graphics. He is
particularly interested in deep learning, efficient optimization
techniques, and structured output prediction.

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