Katerina Fragkiadaki Home

Katerina Fragkiadaki

I am interested in building machines that understand the stories that videos portray, and, inversely, in using videos to teach machines about the world. The pen-ultimate goal is to build a machine that understands movie plots, and the ultimate goal is to build a machine that would want to watch Bergman over this.

I am an Assistant Professor in the Machine Learning Department at Carnegie Mellon. Prior to joining MLD's faculty I spent three wonderful years as a post doctoral researcher first at UC Berkeley working with Jitendra Malik and then at Google Research in Mountain View working with the video group. I completed my Ph.D. in GRASP, UPenn with Jianbo Shi. I did my undergraduate studies at the National Technical University of Athens and before that I was in Crete.

Dear prospective graduate students: There is no need to email, please submit your application to our School following the links here: MLD Ph.D. or R.I. Ph.D.

8015 Gates Hillman
Carnegie Mellon University
Pittsburgh, PA, 94720-1758
katef 'at' cs.cmu.edu

Teaching

Language Grounding for Vision and Control Fall 2017

Deep Reinforcement Learning and Control Spring 2017

Selected Publications

	Self-supervised Learning of Motion Capture Hsiao-Yu Fish Tung, Wei Tung, Ersin Yumer, Katerina Fragkiadaki NIPS 2017, spotlight paper coming soon
	Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and mage-to-Image Translation from Unpaired Supervision Hsiao-Yu Fish Tung, Adam Herley, William Seto and Katerina Fragkiadaki ICCV 2017 paper
	SfM-Net: Learning of Structure and Motion from Video Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar and Katerina Fragkiadaki arxiv
	Motion Prediction Under Multimodality with Conditional Stochastic Networks Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco and Rahul Sukthankar arxiv \| video results
	Learning Feature Hierarchies from Long-Range Temporal Associations in Videos Panna Felsen, Katerina Fragkiadaki, Jitendra Malik and Alexei Efros Workshop on Transfer and Multi-task Learning, in conjunction with NIPS 2015 paper
	Learning Predictive Visual Models of Physics for Playing Billiards Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine and Jitendra Malik ICLR 2016 paper \| project page
	Recurrent Network Models for Human Dynamics Katerina Fragkiadaki, Sergey Levine, Panna Felsen and Jitendra Malik ICCV 2015 paper \| project page
	Human Pose Estimation with Iterative Error Feedback Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, and Jitendra Malik arXiv paper\|project page with code
	Learning to Segment Moving Objects in Videos Katerina Fragkiadaki, Pablo Arbelaez, Panna Felsen and Jitendra Malik CVPR 2015 paper \| poster \| bibtex \| project page with code
	Grouping-Based Low-Rank Video Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, and Jitendra Malik NIPS 2014 paper \| poster \| bibtex \| project page with code
	Two Granularity Tracking: Mediating Trajectory and Detection Graphs for Tracking under Occlusions Katerina Fragkiadaki, Weiyu Zhang, Geng Zhang, and Jianbo Shi ECCV 2012 paper \| poster \| bibtex \| project page with code
	Video Segmentation by tracing Discontinuities in a Trajectory Embedding Katerina Fragkiadaki, Geng Zhang, and Jianbo Shi CVPR 2012 paper \| poster \| bibtex \| project page with code
	Detection-free Tracking: Exploiting Motion and Topology for Segmenting and Tracking under Entanglement Katerina Fragkiadaki and Jianbo Shi CVPR 2011 paper \| poster \| bibtex

Current Graduate Students

Adam Harley, R.I., Ph.D.

Fish Tung, MLD, Ph.D.

Arpit Agarwal, R.I, Masters

Henry Huang, MLD, Masters

MISC

Link to the 10th Perceptual Organization for Computer Vision Workshop in conjunction with CVPR 2016 with a focus on the role of feedback and sequential processing for recognition and motion perception
Link to the 2nd International Workshop on Video Segmentation in conjunction with ECCV 2016 with a focus the role of segmentation for driving learning