<html><head><style type='text/css'>p { margin: 0; }</style></head><body><div style='font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000'>Colloquium Speaker<br>Kristen Grauman, University of Texas at Austin<br>Wednesday, December 3, 4:30pm<br>Computer Science 105<br><br>Searching and Browsing Visual Data<br><br>Widespread
visual sensors and unprecedented connectivity have left us awash with
visual data---from online photo collections, home videos, news footage,
medical images, or surveillance feeds. How can we efficiently browse
image and video collections based on semantically meaningful criteria?
How can we bring order to the data, beyond manually defined keyword
tags? I will present work exploring these questions in the context of
interactive visual search and summarization. <br><br>
<p>In particular, I’ll first introduce attribute representations that
connect visual properties to human describable terms. I’ll show how
these attributes enable both fine-grained content-based retrieval as
well as new forms of human supervision for recognition problems. Then,
I’ll overview our recent work on video summarization, where the goal is
to automatically transform a long video into a short one. Using videos
captured with egocentric wearable cameras, we’ll see how hours of data
can be distilled to a succinct visual storyboard that is understandable
in just moments. Together, these ideas are promising steps towards
widening the channel of communication between humans and computer vision
algorithms, which is critical to facilitate efficient browsing of
large-scale image and video collections.</p><p><br></p>
<p>This is work done with Adriana Kovashka, Yong Jae Lee, Devi Parikh, Lu Zheng, Bo Xiong, and Dinesh Jayaraman.</p>
<p>Kristen Grauman is an Associate Professor in the Department of
Computer Science at the University of Texas at Austin. Her research in
computer vision and machine learning focuses on visual search and object
recognition. Before joining UT-Austin in 2007, she received her Ph.D.
in the EECS department at MIT, in the Computer Science and Artificial
Intelligence Laboratory. She is an Alfred P. Sloan Research Fellow and
Microsoft Research New Faculty Fellow, a recipient of NSF CAREER and ONR
Young Investigator awards, the Regents' Outstanding Teaching Award from
the University of Texas System in 2012, the PAMI Young Researcher Award
in 2013, the 2013 Computers and Thought Award from the International
Joint Conference on Artificial Intelligence, and a Presidential Early
Career Award for Scientists and Engineers (PECASE) in 2013. She and her
collaborators were recognized with the CVPR Best Student Paper Award in
2008 for their work on hashing algorithms for large-scale image
retrieval, and the Marr Best Paper Prize at ICCV in 2011 for their work
on modeling relative visual attributes.</p>
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