Visual Physics @ CVPR 2020
Date: June 19, 2020
Click Here for the Zoom Webinar!
Dear Attendees,
Register for the Zoom Webinar at 1pm on June 19th: https://ucla.zoom.us/webinar/register/WN_dlX3cldoQwe0kcm0UwW1Tw
UCLA PhD Students helping to coordinate include Pradyumna Chari and Yunhao Ba. Thank you and look forward to seeing you on zoom!
--- the organizers
Tutorial Schedule
- 1:00 - 2:00PM (PT)
- Opening Panel - literature will be surveyed and papers will be suggested for reading during the break
- •
- Bill Freeman, MIT
- •
- Achuta Kadambi, UCLA
- •
- Laura Waller, Berkeley
- •
- Ayan Chakrabarti, WUSTL
- •
- Katerina Fragkiadaki, CMU
- 2:00 - 3:30PM (PT)
- Break - selected papers for reading are listed at the bottom of the webpage
- 3:30 - 4:30PM (PT)
- Closing Panel - we will go deeper into literature after folks have had a chance to read, and suggest future trends for the community.
Abstract: The early days of computer vision used "physics-inspired algorithms" to detect contours, edges, faces, and other features. Over the past decade, these structured algorithms have been superseded by deep learning algorithms with superior performance. This tutorial discusses an increasingly popular class of hybrid methods that blend physics and learning. In a unique virtual format, we feature 5 faculty members who provide a joint opening panel and closing panel, with a flipped classroom style reading group in the middle break. A happy hour will conclude the tutorial.
Organizers
Achuta Kadambi
Assistant Professor at UCLA
Bill Freeman
Thomas and Gerd Perkins Professor at MIT
Ayan Chakrabarti
Assistant Professor at WUSTL
Selected Papers for Reading
Visual Physics: Discovering Physical Laws from Videos
Phase-Based Video Motion Processing
Turning Corners into Cameras: Principles and Methods
Physics-Based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging
Learning Spatial Common Sense with Geometry-Aware Recurrent Networks
Training Image Estimators without Image Ground-Truth
Learning visual predictive models of physics for playing billiard
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects
A theory of fermat paths for non-line-of-sight shape reconstruction
TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
Interaction networks for learning about objects, relations and physics
Learning Spatial Common Sense with Geometry-Aware Recurrent Networks
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
Auto-Tuning Structured Light by Optical Stochastic Gradient Descent
How to Do Physics-based Learning
Integrating Physics-Based Modeling with Machine Learning: A Survey
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning
AI Feynman: A physics-inspired method for symbolic regression
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Discovering Symbolic Models from Deep Learning with Inductive Biases
Learning Topology From Synthetic Data for Unsupervised Depth Completion
Blending Diverse Physicsl Priors With Neural Networks
Learning From Synthetic Data: Addressing Domain Shift for Semantic Segmentation
