Event Location: NSH 1507
Bio: After leaving sunny Phoenix, AZ, Bryan received his A.B. from Dartmouth
College. He recently defended his dissertation “Labeling, Discovering,
and Detecting Objects in Images” at MIT under the supervision of William
Freeman and Antonio Torralba. His next journey will be as a post-doctoral
fellow at Ecole Normale Supérieure under Jean Ponce and Andrew Zisserman.
There, he will continue to pursue research in visual object recognition
and scene understanding.

Abstract: Current object recognition systems can only recognize a limited number of
object categories; scaling up to many categories is the next challenge in
object recognition. We seek to build a system to recognize and localize
many different object categories in complex scenes. We achieve this
through a deceptively simple approach: by matching the input image, in an
appropriate representation, to images in a large training set of labeled
images. This gives us a set of retrieval images, which provide hypotheses
for object identities and locations. We combine this knowledge from the
retrieval images with an object detector to detect objects in the image.
The simplicity of the approach allows learning for a large number of
object classes embedded in many different scenes. We demonstrate improved
classification and localization performance over a standard object
detector using a held-out test set from the LabelMe database.
Furthermore, our system restricts the object search space and therefore
greatly increases computational efficiency.