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<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>When:
Thursday,
May 29 @ 3:00pm<b><span style='font-weight:bold'><o:p></o:p></span></b></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Where:
TTI-C Conference Room, <st1:Street w:st="on"><st1:address w:st="on">1427 E. 60<sup>th</sup>
St.</st1:address></st1:Street>, 2<sup>nd</sup> Floor<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p style='margin:0in;margin-bottom:.0001pt'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>Who:
Alan Yuille, UCLA Statistics Department<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Topic:
Discriminative Learning for Deformable Object Parsing<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Work with (Leo) Log Zhu.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>We propose hierarchical models for representing shape and
appearance of deformable objects. These models are learnt in a discriminative
manner using techniques such as structure-perceptron and max-margin. We
describe rapid inference algorithms which can parse these models efficiently.
These approaches are demonstrated on horses, humans, faces and a range of other
objects. We evaluate the effectiveness of these models on standard benchmarked
databases for tasks such as detection, segmentation, matching and parsing.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Contact:
David McAllester,
TTI-C mcallester@tti-c.org
702-5562<o:p></o:p></span></font></p>
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face=Arial><span style='font-size:12.0pt;font-family:Arial;color:navy'><o:p> </o:p></span></font></p>
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style='font-size:12.0pt;font-family:Arial'><o:p> </o:p></span></font></p>
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10.0pt;font-family:Arial;color:navy'><o:p> </o:p></span></font></p>
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