Overview
The field of statistical pattern recognition has evolved significantly in the modern era of Generative AI (GenAI), propelled by advances in computational power, algorithm development, and the availability of large datasets. We will take an introspective look at the evolution of the field through case studies of classical computer vision problems such as object detection and video analytics. We will review the traditional challenges in statistical pattern recognition, including the curse of dimensionality, over-fitting, and the semantic gap between low-level features and high-level semantics, and discuss whether recent strategies in GenAI have addressed these challenges. Rather than providing definitive conclusions, this talk aims to spark discussions on the limitations of GenAI and potential future research directions.
Speaker
Dr. Junsong Yuan is a Professor and the Director of the Visual Computing Lab at the Department of Computer Science and Engineering, State University of New York (SUNY) at Buffalo, USA. Before joining SUNY Buffalo, he was a faculty member at Nanyang Technological University (NTU), Singapore. He obtained his Ph.D. from Northwestern University, the M.Eng. degree from the National University of Singapore, and the B.Eng. degree from Huazhong University of Science Technology (HUST). He is a recipient of the Chancellor’s Award for Excellence in Scholarship and Creative Activities from SUNY, the Nanyang Assistant Professorship from NTU, the Outstanding EECS Ph.D. Thesis Award from Northwestern University, and the Best Paper Award from the IEEE Trans. on Multimedia. He serves as the Senior Area Editor of the Journal of Visual Communication and Image Representation (JVCI) and an Associate Editor of the IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI).
Event details
Title: An Introspective on Statistical Pattern Recognition in the Age of Generative AI
Speaker: Dr. Junsong Yuan, Professor, State University of New York (SUNY), USA
Date: 9 September, 2024 (Monday)
Time: 3:30 PM to 5:00 PM, In Person / Online
Location: ITB A113A/B or Zoom Link