Natural language research announcements

["Samdani, Rajhans" <rsamdan2 AT illinois.edu>]

Hi all!


This is a gentle reminder for today's talk by Prof. Kristen Prof. Kristen 
Grauman (http://www.cs.utexas.edu/~grauman/) in **2405 SC**

Following are the details of her talk:

When: Friday, Dec 2, 2 pm.

Where: 2405 Siebel Center (note: it's not 3405, the usual room, but 2405)

Title:
Capturing Human Insight for Visual Learning

Abstract:
How should visual learning algorithms exploit human knowledge?  Existing 
approaches allow only a narrow channel of input to the system, typically 
treating human annotators as “label machines” who provide category names for 
image or video exemplars.  While there is no question that human 
understanding of visual content is much richer than mere labels, the 
challenge is how to elicit their insight in ways that learning algorithms can 
readily exploit.

We propose to widen the channel of communication to visual recognition 
systems beyond traditional labels.  I will present new techniques that allow 
a human annotator to teach the system more fully---using either descriptive 
relative comparisons (e.g., “bears are furrier than rats”), explanations 
behind the label he assigns to an exemplar (e.g., “this region is too round 
for it to be a chair”), or even through implicit cues about relative object 
importance that are revealed in the way an annotator tags an image.  In 
developing these techniques, we introduce a novel approach to model relative 
visual attributes using learned ranking functions, which generalizes previous 
strategies restricted to categorical properties.  In addition, we investigate 
new cross-modal textual/visual representations that can capture what human 
viewers find most noteworthy in images.  Through results on challenging image 
recognition and retrieval tasks, I will demonstrate the clear advantage of 
incorporating such richer forms of input for visual learning.

This talk describes work with Sung Ju Hwang, Devi Parikh, and Jeff Donahue.

BIO:
Kristen Grauman is a Clare Boothe Luce Assistant 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 a Microsoft Research New Faculty Fellow, and a recipient 
of an NSF CAREER award and the Howes Scholar Award in Computational Science.  
Grauman and her collaborators were awarded the CVPR Best Student Paper Award 
in 2008 for work on hashing algorithms for large-scale image retrieval, and 
the Marr Prize at ICCV in 2011 for work on modeling relative visual 
attributes.

See you there!
Rajhans
_______________________________________________
aiis mailing list
aiis AT cs.uiuc.edu
http://lists.cs.uiuc.edu/mailman/listinfo/aiis