-演講時間: 115年4月1日（三） 14:00~16:00

-演講地點: E6-A207教室

-演講者: 黃郁惠 助理教授(中央大學電機工程系)

-演講主題: Seeing the World : Music Scores, City Streets, and Microchips

-演講摘要: This talk examines how computer vision methods evolve as data availability. We begin before deep learning, showing how an optical music recognition (OMR) system can be built with classical pipelines when labeled data are scarce. We then move into the deep learning era with context-aware padding prediction for scene parsing in autonomous driving, using data to learn boundary context rather than relying on fixed padding rules. Finally, we discuss applying foundation models to wafer skeleton identification in semiconductor packaging, where events are rare and traceability is strict. Across these domains, we present a practical playbook: match your method to your data regime—add rules when labels are limited, scale and regularize when data are abundant but long-tailed, and leverage pretrained priors when supervision is costly.

講者簡介：Yu-Hui Huang is an Assistant Professor in the Department of Electrical Engineering at National Central University, Taiwan. She earned her Ph.D. in Electrical Engineering from KU Leuven, Belgium, and her M.Sc. from RWTH Aachen University, Germany. Her research focuses on computer vision and deep learning, with specific expertise in scene understanding, motion analysis, and optical music recognition. Prior to joining NCU, she served as an Assistant Professor at Yuan Ze University.