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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.
8107 Gates Hillman
Carnegie Mellon University
Pittsburgh, PA, 15213
katef 'at' cs.cmu.edu
Admin: Laura Winter, lwinter@andrew.cmu.edu
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News:
- I gave a talk on ego-stabilized visual learning at the Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM and on Embodied visual learning with neural 3D scene representations at the 3D Scene Understanding for Vision, Graphics, and Robotics. Check out also the visual physics tutorial .
- I got an NSF CAREER award from NSF IIS. Special thanks to Chris, Phil, and Adam for their help, and for making the submission preparation way more fun than what it is supposed to be.
- I am an area chair for ICML 2020, NeurIPS 2020, CVPR 2021
- Workshop on common sense of a toddler coming up in CVPR 2020.
- I was awarded a Sony faculty research award 2019
- I gave a talk on Embodied Visual Recognition at Google Seattle, UberATG, and RobustAI, 40 years anniversary of GRASP lab
- Area chair CVPR 2020
- I gave a talk on Adversarial Inverse Graphics Networks at Augmenting humans workshop, CVPR 2019
- I gave a talk on Embodied Visual recognition at Bringing Robots to the Computer Vision Community Workshop, CVPR, 2019
- I gave a talk on Embodied language Grounding at The How2 Challenge: New Tasks for Vision and Language Workshop, ICML 2019
- I gave a talk on geometry-aware deep visual learning at the theory and practice in ML and CV workshop at ICERM, slides and video are available.
- I was awarded a UPMC faculty research award 2019
- Ricson is selected as a runner-up of the CRA Outstanding Undergraduate Researcher Award for 2019 !
- Area chair ICML 2019, ICLR 2019
- We are organizing an AI4ALL summer school to expose rising juniors to AI and the good it can do for the world, as we see it, here in CMU!
- Area chair CVPR 2018
- I was awarded a Google faculty award 2018
Teaching
Research
Teaching machines to appreciate Bergman is a hard problem, in fact, most people fail in doing so. Thus, it is a very long term goal of our research. Meanwhile, we are working on teaching machines basic common sense. Our recent works use recurrent nets with 3D representation bottlenecks that disentangle camera motion from appearance, while being optimized end-to-end for a final task, such as view prediction or 3D object detection. In this way, our models learn object permanence, size constancy, occlusions and dis-occlusion relationships, useful for 3D object detection, affordance learning and language grounding.
Selected Publications
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Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping
Adam W. Harley, Shrinidhi K. Lakshmikanth, Paul Schydlo, Katerina Fragkiadaki
ECCV 2020
paper
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Embodied Language Grounding with Implicit 3D Visual Feature Representations
Mihir Prabhudesai*, Hsiao-Yu Fish Tung*, Syed Ashar Javed*, Maximilian Sieb, Adam W. Harley, Katerina Fragkiadaki
CVPR 2020
paper | project page
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Graph-structured Visual Imitation
Xian Zhou*, Max Sieb*, Audrey Huang, Oliver Kroemer, Katerina Fragkiadaki
CoRL 2019
paper | project page with code
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Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
Adam W. Harley, Fangyu Li, Shrinidhi K. Lakshmikanth, Xian Zhou, Hsiao-Yu Fish Tung, Katerina Fragkiadaki
ICLR 2020
paper | project page with code
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Learning Spatial Common Sense with Geometry-Aware Recurrent Networks
Hsiao-Yu Fish Tung, Ricson Cheng, Katerina Fragkiadaki
CVPR 2019, oral presentation
paper | project page with code
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Model Learning for Look-ahead Exploration in Continuous Control
Arpit Agarwal, Katharina Muelling and Katerina Fragkiadaki
AAAI 2019, oral presentation
paper | project page with code
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Data Dreaming for Object Detection: Learning Object-Centric State Representations for Visual Imitation
Maximilian Sieb and Katerina Fragkiadaki
Humanoids 2018, oral presentation
paper | slides
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Reinforcement Learning of Active Vision for Manipulating Objects under Occlusionss
Ricson Cheng, Arpit Agarwal, and Katerina Fragkiadaki
CoRL 2018
paper | slides | code
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Geometry-Aware Recurrent Neural Networks for Active Visual Recognition
Ricson Cheng, Ziyan Wang, and Katerina Fragkiadaki
NIPS 2018
paper
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Reward Learning from Narrated Demonstrations
Fish Tung, Adam Harley, Liang-Kang Huang, Katerina Fragkiadaki
CVPR 2018
paper | bibtex
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Depth-adaptive Computational Policies for Efficient Visual Tracking
Chris Ying, Katerina Fragkiadaki
EMMCVPR 2017
paper | bibtex
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Self-supervised Learning of Motion Capture
Hsiao-Yu Fish Tung, Wei Tung, Ersin Yumer, Katerina Fragkiadaki
NIPS 2017, spotlight
paper | bibtex | code
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Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and mage-to-Image Translation from Unpaired Supervision
Hsiao-Yu Fish Tung, Adam Harley, William Seto and Katerina Fragkiadaki
ICCV 2017
paper | bibtex | code
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SfM-Net: Learning of Structure and Motion from Video
Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar and Katerina Fragkiadaki
arxiv
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Motion Prediction Under Multimodality with Conditional Stochastic Networks
Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco and Rahul Sukthankar
arxiv | video results
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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
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Learning Predictive Visual Models of Physics for Playing Billiards
Katerina Fragkiadaki*, Pulkit Agrawal*, Sergey Levine and Jitendra Malik
ICLR 2016
paper | project page
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Recurrent Network Models for Human Dynamics
Katerina Fragkiadaki, Sergey Levine, Panna Felsen and Jitendra Malik
ICCV 2015
paper |
project page
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Human Pose Estimation with Iterative Error
Feedback
Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, and Jitendra Malik
arXiv
paper|project page with code
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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
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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
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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
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Video Segmentation by tracing Discontinuities in a Trajectory Embedding
Katerina Fragkiadaki, Geng Zhang, and Jianbo Shi
CVPR 2012
paper
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poster |
bibtex |
project page with code
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Detection-free Tracking: Exploiting Motion and Topology for Segmenting and Tracking under Entanglement
Katerina Fragkiadaki and Jianbo Shi
CVPR 2011
paper
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poster
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bibtex
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Current Students
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Adam Harley, R.I. Ph.D.
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Fish Tung, MLD Ph.D.
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Paul Rudolph Schydlo , CMU-Portugal program, coadvised by José Santos-Victor
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Ashwini Polke, MLD Ph.D.
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Xian Zhou, R.I. Ph.D.
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Mihir Prabhudesai , research associate
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Shamit Lal, Masters MSCV
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Yiming Zuo , Masters MSR
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Darshan Patil , research associate
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Gaurav Pathak, research associate
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Shrinidhi Kowshika Lakshmikanth, research associate
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Alumni
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Ricson Chen, undergrad CSD
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Ziyan Wang, R.I. Masters
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Max Sieb, R.I. Masters
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Arpit Agarwal, R.I. Masters
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Henry Huang, MLD Masters
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Chris Ying, MLD Masters
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Yijie Wang, MLD Masters
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MISC
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