The success of deep learning in computer vision is partly attributed to the constructio nof large-scale labeled datasets such as ImageNet. However, conputer vision problems often require substantial human effrots and interventions to obtain accurate annotations due to dynamic aspects of class labels, needs for pixel-level labeling, and annotation ambiguities. Hence, colelcting high quality large-scale laveled datasets is very time consuming and even infeasible. This talk mainly addressess challenges and solutions related to data deficiency issue in the ocntext of semantic segmentation, where we can tak advantage of various types of weakly supervision. Specifically, I present a weakly supervised semantic segmentation algorithms with automatic data augmentation using videos. The proposed algorithm does not require any segmentation labels but show the state-of-the-art performance in the standard dataset. I also discuss weakly supervised learning in other problems including representation learning, multi-modal learning, online learning, etc.
Bohyung Han recieved the B.S. and M.S. degrees from the Department of Computer Engineering at Seoul National Universitiy, Korea, in 1997 and 2000, respectively, and the Ph.D. degree from the Department of Computer Science at the University of Maryland, College Park, MD, USA, in 2005. He is currently an Associate Professor with the Department of Computer Science and Engineering at POSTECH, Korea, and a Vising Researcher at Google. Prior to the current position, he was with Samsung, Irvine, CA, USA, and Mobileye Vision Technologies, Princetone, NJ, USA. He served or will ve srving as an Area Char in ICCV 2017, CVPR 2017, NIPS 2015, ICCV 2015, ACCV 2012/2014/2016, ACML 2016 and WACV 2014/2017, a Tutorial Chair in ICCV 2019, and a Demo Chair in ACCV 2014. He is also serving as Area Editor in Computer Vision and Image Understanding and Associate Editor in Machine Vision Applications. His current research interests include computer vision and machine learning with emphasis on deep learning.