Friday, January 27, 2017 - 2:30pm


2315 Doherty Hall


Jason Corso University of Michigan

Event Website:

Jason Corso

Sparse Modeling in Deep and Cross-Modal Embeddings

ABSTRACT  Joint video-language modeling has been attracting increasing attention in recent years, signifying a return to early AI goals of cooperative cognitive systems.  However, many approaches fail to leverage the complementarity and structure across vision and language.  For example, they may rely on a fixed visual model or fail to leverage the underlying compositional semantics inherent in language.  In this talk, I will discuss indeed seek to explicitly jointly capture structure across modalities, and to capture this structure at a low-level.  The work explores sparse modeling as a means for bridging across vision and language.  These are low-level models that capture a joint, generative embedding using paired and composition dictionary learning.  We also overcome a historical limitation of such sparse models by showing how they can be embedded directly within a deep artificial neural network.  Results for both of these works will be provided and discussed in detail.

BIO Jason Corso is an associate professor of Electrical Engineering and Computer Science at the University of Michigan.  He received his PhD and MSE degrees at The Johns Hopkins University in 2005 and 2002, respectively, and the BS Degree with honors from Loyola College In Maryland in 2000, all in Computer Science.  He spent two years as a post-doctoral fellow at the University of California, Los Angeles.

From 2007-14 he was a member of the Computer Science and Engineering faculty at SUNY Buffalo.  He is the recipient of a Google Faculty Research Award 2015, the Army Research Office Young Investigator Award 2010, NSF CAREER award 2009, SUNY Buffalo Young Investigator Award 2011, a member of the 2009 DARPA Computer Science Study Group, and a recipient of the Link Foundation Fellowship in Advanced Simulation and Training 2003.  Corso has authored more than one-hundred peer-reviewed papers on topics of his research interest including computer vision, robot perception, data science, and medical imaging.  He is a member of the AAAI, ACM, MAA and a senior member of the IEEE.


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