[Intelligence Seminar] March 30: Fei-Fei Li, GHC 4303, 3:30, "Story Telling in Images: Modeling Visual Hierarchies Within and Across Images"

Noah A Smith nasmith at cs.cmu.edu
Tue Mar 23 12:07:52 EDT 2010

Intelligence Seminar

Tuesday, March 30, 2010
GHC 4304
Host:  Eric Xing
Please contact Michelle Martin (michelle324 at cs.cmu.edu) for meetings.

Title:  Story Telling in Images: Modeling Visual Hierarchies Within
and Across Images
Fei-Fei Li, Stanford University

The human visual system is extremely good at perceiving and
understanding the meaning of the visual world. This includes object
recognition, scene classification, image segmentation, motion
analysis, activity, event understanding, and many more tasks. Pixels
in images, and images in the visual world, are not organized in random
ways. The human visual system processes information in a hierarchy of
visual areas, most likely to achieve efficient and effective
processing of the data. In a similar vein, we show that hierarchical
representation of the pixel space can be an effective way of modeling
increasingly complex visual scenes. We start with a quick review of
two past work in basic-level scene classification. Then we show that
by putting together over-segmented image regions, objects (and tags)
and scenes, we make progress on three fundamental visual recognition
tasks (scene classification, object annotation and segmentation) in
one coherent, probabilistic model. In an upcoming CVPR paper, we focus
on using a hierarchical representation to discover important
connectivity between parts of a human body to the object that
interacts with the person (e.g. pitching baseball). This hierarchical
representation is very effective in providing mutual context to
detecting objects and estimating human poses, both are extremely
difficult tasks in cluttered visual scenes. And finally, in another
upcoming CVPR paper, we show an automatic way of organizing a large
number of photographs downloaded from Flickr in a semantically
meaningful hierarchy.  hierarchy can serve as a useful knowledge
structure for visual tasks such as scene classification and


Prof. Fei-Fei Li's main research interest is in vision, particularly
high-level visual recognition. In computer vision, Fei-s interests
span from object and natural scene categorization to human activity
categorizations in both videos and still images. In human vision, she
has studied the interaction of attention and natural scene and object
recognition, and decoding the human brain fMRI activities involved in
natural scene categorization by using pattern recognition
algorithms. Fei-Fei graduated from Princeton University in 1999 with a
physics degree. She received PhD in electrical engineering from the
California Institute of Technology in 2005. From 2005 to August 2009,
Fei-Fei was an assistant professor in the Electrical and Computer
Engineering Department at University of Illinois Urbana-Champaign and
Computer Science Department at Princeton University, respectively. She
is currently an Assistant Professor in the Computer Science Department
at Stanford University. Fei-Fei is a recipient of a Microsoft Research
New Faculty award and an NSF CAREER award. (Fei-Fei publishes using
the name L. Fei-Fei.)

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