Computer Imaging Seminar

— 1:30pm

In Person - Smith Hall 236

PRADYUMNA CHAIR, Ph.D. Candidate, Visual Machines Group, Department of Electrical and Computer Engineering, University of California, Los Angeles

Imaging and Vision in the Era of Large-Scale Foundation Models

Foundation models leverage the strength of self-supervised learning on large data volumes to learn complex real-world patterns. These large models have been found to have multi-task generalization capabilities and are widely regarded as the future of computer vision and AI. A pertinent question is whether scale is the solution to all problems, in which case the benefits of computational imaging are largely rendered ineffective. We argue that this is not the case. We begin by discussing classes of problems where unbounded scale is a fundamentally incapable solution, using the case study of equitable imaging. We will then discuss classes of problems where unbounded scale is not practical, using the case study of medical imaging. Finally, we will discuss the interplay between imaging, vision and large-scale foundation models. With this context, we will discuss the future of computational imaging, in the era of large foundation models. 


Pradyumna Chari is a 5th year PhD student at the UCLA Visual Machines Group, advised by Prof. Achuta Kadambi. His research lies at the intersection of computational imaging, computer vision and fair ML with applications in health sensing. At UCLA, Pradyumna has received the CISCO PhD Fellowship, UCLA Graduate Dean’s Scholar Award, and a UCLA ECE Department Fellowship. Prior to UCLA, Pradyumna completed his undergraduate studies at the Indian Institute of Technology Madras, where he received the President of India Gold Medal and a Siemens Prize for outstanding academic performance. 

Faculty Host: Aswin Sankaranarayanan  

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