[2017-12-29] Prof. Jia-Bin Huang,Virginia Tech, “Learning Visual Reconstruction”


Title: Learning Visual Reconstruction
Date: 2017-12-29  11:00 am-12:00 am
Location: R102, CSIE
Speaker: Jia-Bin Huang,Virginia Tech.
Hosted by: Prof. Yung-Yu Chuang




​​Deep learning approaches have demonstrated impressive results in a wide variety of visual recognition tasks. The successes mainly result from the use of the massive human-labeled data such as the ImageNet. However, in visual reconstruction tasks - recovering 3D scene geometry, dense motion, material, surface normals, and illumination conditions from one or multiple images of a dynamic scene - large-scale ground truth labels are often difficult or impossible to obtain. In this talk, I will give three examples of tackling visual reconstruction using learning-based approaches. To address the dataset problem, I will demonstrate how we can leverage simulation, reconstruction, and consistency as supervisory signals. I will close by listing several exciting open problems.



Jia-Bin Huang is an assistant professor in the Bradley Electrical and Computer Engineering at Virginia Tech. He received the B.S. degree in Electronics Engineering from National Chiao-Tung University, Hsinchu, Taiwan and his Ph.D. degree in the Department of Electrical and Computer Engineering at University of Illinois, Urbana-Champaign. His research interests include computer vision, computer graphics, and machine learning. His research received the best student paper award in IAPR International Conference on Pattern Recognition (ICPR) in 2012 for the work on computational modeling of visual saliency and the best paper award in the ACM Symposium on Eye Tracking Research & Applications (ETRA) in 2014 for work on learning-based eye gaze tracking.

Personal webpage: https://filebox.ece.vt.edu/~jbhuang

最後修改時間:2017-12-25 PM 5:30

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