Abstract

Deep learning has shown its success in various tasks in areas such as computer vision and natural language processing. However, while deep learning models trained in fully-supervised fashions have exhibited promising performances, how to design and train such models with small or unsupervised data remains a challenging task. In this talk, I will go over recent trends for deep learning (particularly in computer vision) on the development of meta and self-supervised learning strategies, which can be applied to tackle the aforementioned challenging yet practical settings. I will also talk about our recent NeurIPS, CVPR, and ECCV works, and explain how we advance these learning schemes for visual analysis.

Bio.

Yu-Chiang Frank Wang received his B.S. degree in Electrical Engineering from National Taiwan University in 2001. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University in 2004 and 2009, respectively. In 2009, Dr. Wang joined the Research Center for Information Technology Innovation (CITI) of Academia Sinica, and later served as a deputy director of CITI. Dr. Wang joined the Department of Electrical Engineering at National Taiwan University in 2017 as an Associate Professor, and was promoted to Professor in 2019. With continuing research focus on computer vision and machine learning, Dr. Wang's recent research topics include deep learning for transfer learning and meta-learning. Dr. Wang serves as organizing committee and area chairs of multiple international conferences such as CVPR, ICCV, ECCV, and ACCV. Several of his papers are nominated for the best paper awards, including IEEE ICIP, ICME, AVSS and MVA. Dr. Wang was twice selected as the Outstanding Young Researcher by the Ministry of Science and Technology of Taiwan (2013-2015 and 2017-2019), and received Excellence in Teaching Award in 2021 and 2022.

All faculty and students are welcome to join.