Advancing Computer Vision in the Era of Vision Foundation Models

Abstract:

Over the past several decades, computer vision has witnessed remarkable progress across a broad range of tasks. Recently, the field has entered a new era with the emergence of large-scale Vision Foundation Models (VFMs). By learning general-purpose visual representations from massive data, VFMs offer new opportunities to address several long-standing challenges in computer vision, including generalization, open-vocabulary understanding, and the development of unified models. In this talk, we will explore the evolving role of VFMs in vision, spanning several paradigms: leveraging frozen VFMs, adapting VFMs, developing domain-specific VFMs, and, ultimately, using VFMs as agents. Together, these approaches point toward a broader shift in how we build, adapt, and deploy computer vision systems.

Bio:

Dr. Xiaoming Liu is Jacqueline Maria Hagan Distinguished Professor at the Department of Computer Science of University of North Carolina at Chapel Hill. He was MSU Foundation Professor, and Anil and Nandita Jain Endowed Professor at the Department of Computer Science and Engineering of Michigan State University (MSU). He received Ph.D. degree from Carnegie Mellon University in 2004. Before joining MSU in 2012 he was a research scientist at General Electric (GE) Global Research. He works on computer vision, machine learning, and biometrics especially on face related analysis and 3D vision. He is an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He has authored more than 200 scientific publications, and has filed 35 U.S. patents. His work has been cited over 35000 times according to Google Scholar, with an H-index of 90. He is a fellow of IEEE and IAPR.  More information of Dr. Liu’s research can be found at http://cvlab.cs.unc.edu