Research in our lab focuses on two intimately connected branches of vision research: computer vision and human vision. In both fields, we are intrigued by visual functionalities that give rise to semantically meaningful interpretations of the visual world.

In computer vision, we aspire to build intelligent visual algorithms that perform important visual perception tasks such as object recognition, scene categorization, integrative scene understanding, human motion recognition, material recognition, etc.

In human vision, our curiosity leads us to study the underlying neural mechanisms that enable the human visual system to perform high level visual tasks with amazing speed and efficiency.


News and Events

December 2013
The ImageNet Large Scale Visual Recognition Challenge 2013 workshop at ICCV 2013 was a success! Slides are now available.
November 2013
The results of the latest ImageNet Large Scale Visual Recognition Challenge (ILSVRC2013) are up! See them here.
March 2013
We are preparing to run the ImageNet Large Scale Visual Recognition Challenge 2013. This year we will be introducing an additional object detection task with 200 object categories modeled after the PASCAL VOC detection challenge.
March 2013
Check out the new Fine-Grained Classification Challenge that will target fine-grained classification in a range of domains.
December 2012
September 2012
We are organizing the Bay Area Vision Meeting (BAVM) 2012.
July 2012
We are organizing the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 . In addition to classification and detection of 1,000 object categories, we introduce a third task on fine grained categorization of 120 dog subcategories.
(Results are now available here)

Press Coverage

Seeking a Better Way to Find Web Images

The New York Times, November 2012

Mind Readering

Stanford University News, May 2011

Sorting Through Photos

ACM Communications, May 2011