Electrical Engineering Seminar

What:      EE Department Seminar

When:     10:00am on Thursday, May 8^th, 2025

Where:    Steinman Hall, Exhibit Room

Speaker: Dr. Wei Tang                                                                                

Abstract:   Computer vision seeks to extract semantic and geometric information from images and videos, serving as the perceptual foundation for intelligent systems such as robots and autonomous vehicles. Over the past decade, deep learning has driven remarkable progress in the field, advancing capabilities from 2D recognition to 3D reconstruction. However, the current purely data-driven paradigm faces fundamental challenges, including data inefficiency, curse of high dimensionality, and limited understanding of visual entities beyond individual objects.

In this talk, I will present my recent research on modeling and learning rich visual structures to address these challenges. First, I will introduce a novel framework that integrates explicit visual dependency modeling with deep learning for 2D and 3D dense prediction. Next, I will demonstrate how unfolding the manifold structure of visual data enables unsupervised semantic segmentation. Finally, I will present a recent project that represents, parses, and learns the geometric compositionality of 3D objects to facilitate self-supervised part-whole reconstruction. Through these efforts, I aim to bridge the gap between data-driven deep learning and visual structure modeling, paving the way for more efficient, generalizable, and interpretable computer vision models.

Bio: Dr. Wei Tang is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago (UIC). He obtained his Ph.D. in Electrical Engineering from Northwestern University, where his dissertation was honored with a Best Dissertation Award. His research interests include computer vision, digital image processing, and machine learning. Dr. Tang has served as an associate editor for several international journals, including Pattern Recognition and Machine Vision and Applications, and as an area chair for leading conferences, including CVPR, ICCV, and WACV. His research has been funded by the National Science Foundation (NSF) and industry partners such as Motorola and Wormpex AI Research. 

RSVP Link: https://forms.gle/WfhANgyQQhudoXrB8