<html><body>Large-Scale Detector Adaptation and Other Recent Results<br><span class="event-speaker">
<a href="http://www.eecs.berkeley.edu/%7Etrevor/" target="_blank">Trevor Darrell</a>, </span><span class="event-speaker-from"><a href="http://berkeley.edu" target="_blank">University of California, Berkeley</a>
</span><br>Thursday, October 16, 4:30pm<br>Computer Science 105<br><br>In this <span>talk</span> I'll review recent progress towards robust
and effective perceptual representation learning. I'll describe new
methods for large-scale detection, whereby robust detectors can be
learned from weakly labeled training data, following paradigms of domain
adaptation and multiple instance learning. I'll discuss how such
models can be used not only for detection but also for pose prediction
and further for effective fine-grained recognition, extending
traditional convolutional neural network models to include explicit
pose-normalized descriptors. Finally, and time permitting (pardon the
pun), I'll review our recent work on anytime recognition, which provides
methods that strive to provide the best answer possible, even with a
limited (and unknown) time budget.<br><br>
<p>Prof. Trevor Darrell’s group is co-located at the University of
California, Berkeley, and the UCB-affiliated International Computer
Science Institute (ICSI), also located in Berkeley, CA. Prof. Darrell is
on the faculty of the CS Division of the EECS Department at UCB and is
the vision group lead at ICSI. Darrell’s group develops algorithms for
large-scale perceptual learning, including object and activity
recognition and detection, for a variety of applications including
multimodal interaction with robots and mobile devices. His interests
include computer vision, machine learning, computer graphics, and
perception-based human computer interfaces. Prof. Darrell was previously
on the faculty of the MIT EECS department from 1999-2008, where he
directed the Vision Interface Group. He was a member of the research
staff at Interval Research Corporation from 1996-1999, and received the
S.M., and PhD. degrees from MIT in 1992 and 1996, respectively. He
obtained the B.S.E. degree from the University of Pennsylvania in 1988,
having started his career in computer vision as an undergraduate
researcher in Ruzena Bajcsy's GRASP lab.</p></body></html>