[Intelligence Seminar] Correction: TODAY: 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 30 11:00:26 EDT 2010

Correction:  today's talk is in GHC 4303, not GHC 4304.  Apologies for the typo.

On Mon, Mar 29, 2010 at 10:24 AM, Noah A Smith <nasmith at cs.cmu.edu> wrote:
> Intelligence Seminar
> Tuesday, March 30, 2010
> 3:30pm
> GHC 4303
> 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
> annotation.
> Bio:
> 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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