[Colloquium] Girshick/Dissertation Defense/Apr 20, 2012

[Margaret Jaffey]

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Ross Girshick

Date:  Friday, April 20, 2012

Time:  2:30 PM

Place:  Ryerson 276

Title: From Rigid Templates to Grammars: Object Detection with
Structured Models

Abstract:
We develop models for localizing instances of a generic object
category, such as cars or people, in images. We define these models
using a grammar formalism. In this formalism compositional rules are
used to encode models that can range in complexity from simple rigid
templates to rich deformable part models with variable structure. A
central contribution of this dissertation is an exploration along this
axis, wherein we gradually enrich our object category representations.
We demonstrate that these richer models lead to improved object
detection performance on challenging datasets such as the PASCAL VOC
Challenges.

While building richer models, we would like to make use of existing
training data and annotations. These annotations typically specify
labels, such as object bounding boxes, that are "weak" compared to the
derivation trees produced by detection with a grammar model. We
propose a discriminative training method that directly supports
learning models from weakly-labeled data. We show how to apply this
formalism to the problem of learning the parameters of a grammar
model. This approach results in a top-performing method for detecting
people in images.

In order to achieve widespread use in research and applications, an
object detection system must not only be accurate, but also fast.
Along the line of efficient computation, we develop a technique for
"compiling" one of our object models into a much faster detector that
implements a cascade architecture. We show how to select the cascade
thresholds in a way that is both safe and effective. We demonstrate
that the cascaded detector produces detections 15x faster than the
non-cascade approach with no loss in precision or recall.

Ross's advisor is Prof. Pedro Felzenszwalb

Login to the Computer Science Department website for details,
including a draft copy of the dissertation:

 https://www.cs.uchicago.edu/phd/phd_announcements#rbg

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Margaret P. Jaffey            margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 156)               (773) 702-6011
The University of Chicago      http://www.cs.uchicago.edu
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