Skip to Content.
Sympa Menu

nl-uiuc - [nl-uiuc] Upcoming talk at the AIIS seminar

nl-uiuc AT lists.cs.illinois.edu

Subject: Natural language research announcements

List archive

[nl-uiuc] Upcoming talk at the AIIS seminar


Chronological Thread 
  • From: Ming-Wei Chang <mchang21 AT uiuc.edu>
  • To: nl-uiuc AT cs.uiuc.edu, aivr AT cs.uiuc.edu, dais AT cs.uiuc.edu, cogcomp AT cs.uiuc.edu, vision AT cs.uiuc.edu, krr-group AT cs.uiuc.edu, aiis AT cs.uiuc.edu
  • Subject: [nl-uiuc] Upcoming talk at the AIIS seminar
  • Date: Mon, 20 Apr 2009 21:35:34 -0500
  • List-archive: <http://lists.cs.uiuc.edu/pipermail/nl-uiuc>
  • List-id: Natural language research announcements <nl-uiuc.cs.uiuc.edu>


Dear faculty and students,

This week we will have Dr. Derek Hoiem, a new assistant professor in CS
department, to have an interesting talk (details below) for the AIIS
seminar at 4:00 pm, Apr 23rd (this Thursday). The room number is
3405. Hope to see you there!

Title:
Inferring Object Attributes

Abstract:
Ultimately, the goal of computer vision is to make useful inferences from
imagery, and a big part of that is knowing something about the properties of
nearby objects. In this talk, I'll describe our recent work on learning to
identify object attributes, such as parts, materials, or shape, from images
in a way that generalizes to new object categories. The tricky part is
training classifiers that really predict the intended attribute, and not
ones that are correlated through familiar object categories. Once we can
predict attributes, we can say what is unusual about an object and more
easily learn to recognize new objects. Sometimes we can even recognize new
object categories from a purely verbal description (e.g., a goat has four
legs, horns, and is furry).

This work is with Ali Farhadi, Ian Endres, and David Forsyth at UIUC.


Speaker Bio:
Derek Hoiem is a new assistant professor at UIUC. He researches object
recognition, segmentation, 3d reconstruction from images, and other
aspects of computer vision that are related to scene understanding.

Best,

Ming-Wei




Archive powered by MHonArc 2.6.16.

Top of Page