Event Actions
Video understanding remains one of the most challenging frontiers in computer vision, with critical applications in autonomous vehicles, security systems, industrial automation, and educational technology. While Transformer networks have revolutionized artificial intelligence - most notably in language models like ChatGPT - their application to video analysis has not yet reached its full potential. This talk introduces innovative architectural enhancements to Transformer networks specifically optimized for video understanding. Unlike traditional Transformers, which were originally designed for natural language processing and later adapted for static images, our approach addresses the unique challenges of temporal information and motion dynamics in video data. Through several novel methods, we demonstrate how these enhanced architectures can better capture the complex relationships between spatial and temporal features in video sequences, potentially opening new possibilities for more sophisticated video analysis systems.
Matthew Korban is a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering at the University of Virginia, specializing in computer vision with a focus on video understanding. He has published in top-tier journals and conferences, including IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, and ECCV. Matthew is an IEEE senior member and serves as an Associate Editor for several respected journals, including Signal, Image and Video Processing, The Visual Computer, and Multimedia Systems. In addition to his research contributions, he has held leadership roles, notably organizing the highly successful UVA Postdoc Symposium from 2021 to 2024.
The Artificial Intelligence and Machine Learning (AIML) seminar provides a communication platform for our colleagues and friends who are working on artificial intelligence, machine learning, and related fields. You can find more information about future talks via the seminar website.