Date & Time:
March 11, 2020 10:30 am – 11:30 am
Location:
Crerar 390, 5730 S. Ellis Ave., Chicago, IL,
03/11/2020 10:30 AM 03/11/2020 11:30 AM America/Chicago Subhransu Maji (UMass) – Task-Specific Recognition by Modeling Visual Tasks Crerar 390, 5730 S. Ellis Ave., Chicago, IL,

Task-Specific Recognition by Modeling Visual Tasks

The AI revolution powered in part by advances in deep learning has led to many successes in the last decade. I’ll describe some of our work in this vein that has enabled us to infer detailed properties of objects in images such as their 3D structure or fine-grained category within a taxonomy, as well as study ecological phenomena at an unprecedented scale using data collected from RADAR networks.

Despite these successes the vast majority of important applications remain beyond the scope of current AI systems. One barrier is that existing algorithms lack the ability to learn from limited data. This is a fundamental challenge because real-world data is dynamic, heavy-tailed, where supervision can be hard to acquire. I argue that a principled framework for reasoning about AI problems can enable modular and data-efficient solutions. Towards this end I’ll describe our framework for embedding computer vision tasks into a vector space that allows us to learn and reason about their properties. Our approach represents a task as the Fisher information of the parameters of a generic “probe” network. We show that the distance between these vectors correlates with natural metrics over tasks. It is also predictive of transfer, i.e., how much does training a deep network on one task benefit another, and can be used for model recommendation. On a portfolio of hundreds of vision tasks the recommended network using our approach outperforms the current gold standard of fine-tuning an ImageNet pre-trained network, especially when training data is limited. I’ll conclude with some of the life-cycle challenges that we need to address to make AI systems widely applicable.

Host: Michael Maire

Subhransu Maji

Assistant Professor, University of Massachusetts Amherst

Subhransu Maji is an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst where he co-directs the Computer Vision Lab. He is also affiliated with the Center of Data Science and AWS AI. Prior to this he was a Research Assistant Professor at TTI Chicago, a philanthropically endowed academic institute in the University of Chicago campus. He obtained his Ph.D. in Computer Science from the University of California at Berkeley in 2011 and B.Tech. in Computer Science and Engineering from IIT Kanpur in 2006. For his work, he has received a Google graduate fellowship, NSF CAREER Award (2018), and a best paper honorable mention at CVPR 2018. He also serves on the editorial board of the International Journal of Computer Vision (IJCV). 

Related News & Events

UChicago CS News

Super.tech/EPiQC Research Informs New Suite of Benchmarks for Quantum Computers

Feb 24, 2022
UChicago CS News

New Assistant Professor Rana Hanocka Combines AI, 3D, and Computer Graphics

Feb 09, 2022
In the News

Quanta Magazine Features Prof. Bill Fefferman’s Work on Quantum Algorithms

Jan 20, 2022
UChicago CS News

UChicago CS Prof. Ben Zhao Named ACM Fellow

Jan 19, 2022
UChicago CS News

CS 4th Year Sophie Veys Receives CRA Undergraduate Research Award

Jan 14, 2022
UChicago CS News

In-Fridge Controller Could Scale Up Quantum Computers, Award-Winning UChicago Research Finds

Jan 10, 2022
UChicago CS News

Prof. Rebecca Willett Named IEEE Fellow

Nov 29, 2021
UChicago CS News

Aaron Elmore Promoted to Associate Professor at UChicago Computer Science

Nov 24, 2021
UChicago CS News

ScaleStuds Project Receives $5 Million to Build Foundations for Massive Computation

Nov 19, 2021
UChicago CS News

Using AI and Data Science to Reliably Detect Internet Censorship in Real Time

Nov 02, 2021
UChicago CS News

EPiQC Research Receives Best Paper Award at IEEE Quantum Week

Oct 22, 2021
UChicago CS News

New Wearable Device Controls Individual Fingers for Sign Language, Music Applications

Oct 11, 2021
arrow-down-largearrow-left-largearrow-right-large-greyarrow-right-large-yellowarrow-right-largearrow-right-smallbutton-arrowclosedocumentfacebookfacet-arrow-down-whitefacet-arrow-downPage 1CheckedCheckedicon-apple-t5backgroundLayer 1icon-google-t5icon-office365-t5icon-outlook-t5backgroundLayer 1icon-outlookcom-t5backgroundLayer 1icon-yahoo-t5backgroundLayer 1internal-yellowinternalintranetlinkedinlinkoutpauseplaypresentationsearch-bluesearchshareslider-arrow-nextslider-arrow-prevtwittervideoyoutube