Abstract: There have been significant advances in computer vision in the past decade. Current computer vision systems usually learn a generic model. In order to handle the diversity of the visual world, the current approach is to scale up the model. Although scaling has been proved effective in the era of large language model, I argue that there are also other alternative approaches we should explore. In this talk, I will introduce some of our recent work on building robust computer vision systems via adaption and continual learning. Instead of learning and deploying one generic model, our goal is to learn a model that can effectively and continuously adapt itself to different environments. I will present applications of this framework in several computer vision applications.
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Yang Wang is currently an associate professor in the Department of Computer Science and Software Engineering, Concordia University. Previously, he was a faculty member at the University of Manitoba. During 2020-2022, he worked as the Chief Scientist in Computer Vision at the Consumer Business Group, Huawei Canada. He did his PhD from Simon Fraser University, MSc from University of Alberta, and BEng from Harbin Institute of Technology. Before joining UManitoba, he worked as a NSERC postdoc at the University of Illinois at Urbana-Champaign. His research focuses on computer vision and machine learning.
