From Illustrations to sports videos: How can we utilize the data?
Pinar Duygulu
(Hacettepe University, Turkey)
Abstract:
In this talk, first I will present our recent efforts on leveraging
large amounts images and videos on the web to design methods that learn
from weakly labeled data. I will describe two methods for organization
and cleaning of the data collected through text based querying and
discuss the results on sport videos. With the focus on soccer videos, I
will also show the results of another study on tracking players. In the
second part, I will introduce a dataset collected from illustrations in
children’s books and discuss about our initial findings.
Short Bio:
Pinar Duygulu has received her BSc, MSc and PhD degrees from Department
of Computer Engineering at Middle East Technical University, Ankara,
Turkey in 1996, 1998 and 2003 respectively. During her PhD, she was a
visiting scholar at University of California at Berkeley under the
supervision of Prof. David Forsyth. After being a post-doctoral
researcher at Informadia Project at Carnegie Mellon University, she
joined to Department of Computer Engineering at Bilkent University,
Ankara, Turkey in 2004. During 2014 and 2015 she was at Carnegie Mellon
University as a research associate. Currently, she is a faculty member
at Department of Computer Engineering at Hacettepe University, Ankara,
Turkey. She received Science Academy's Young Scientist Award (BAGEP) in
2015, Fulbright scholarship in 2013, TUBITAK Career award in 2005, and
the best paper in Cognitive Vision award at European Conference on
Computer Vision in 2002. Her current research interests include
computer vision and multimedia data mining, specifically object, face
and action recognition in large image and video collections and
analysis of historical documents