Ximeng Sun posted the code for our paper “A Temporally-Aware Interpolation Network for Video Frame Inpainting” on GitHub. Check it out here!
I am a Computer Science and Engineering Ph.D. candidate at the University of Michigan in Ann Arbor, working with Prof. Jason J. Corso in computer vision. I am interested in structured deep learning, especially applied to vision and language problems. In 2017, I did an internship at Toyota Research Institute in Cambridge under the advisement of Dr. Simon Stent and Dr. German Ros, where I compared the modeling and generalization performance of multiple state-of-the-art video prediction networks with a novel dataset.
In 2015, I graduated summa cum laude from the University of Massachusetts in Amherst, where I received B.S. degrees in Computer Science and Mathematics. At UMass, I did research as part of the RIPPLES lab under Prof. W. Richards Adrion and Prof. Paul E. Dickson. I worked on various aspects of the lab's Presentations Automatically Organized from Lectures (PAOL) system, including video conversion, whiteboard processing, multithreading, and the graphical user interface. For my Honors Thesis project, I proposed and evaluated a novel technique for segmenting whiteboard marker strokes in real time. Additionally, I was briefly a member of the Center for e-Design under the instruction of Prof. Jack C. Wileden and Prof. Sundar Krishnamurty, where I worked on a program that converted models between Computer-Aided Design systems.
Awards and Honors
- NSF Graduate Research Fellowship 2017 - Honorable Mention (UMich)
- Outstanding Achievement in Artificial Intelligence Award (UMass)
- Honors Dean's Award (UMass)
- Honors Research Grant (UMass)
- Research Assistant Fellowship (UMass)
My paper “A Dataset To Evaluate The Representations Learned By Video Prediction Models” has been accepted to ICLR 2018 workshop track!
Ximeng Sun and I submitted the paper “A Temporally-Aware Interpolation Network for Video Frame Inpainting” to arXiv.
My paper “Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation” has been accepted to ICCV 2017!
The code and keypoint data for my ArXiv paper “Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation” is now available.