no code implementations • ICCV 2017 • George Leifman, Dmitry Rudoy, Tristan Swedish, Eduardo Bayro-Corrochano, Ramesh Raskar
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing videos that contain a depth map (RGBD) on a 2D screen.
1 code implementation • 30 Oct 2018 • Zhijing Jin, Tristan Swedish, Ramesh Raskar
Over the recent years, there has been an explosion of studies on autonomous vehicles.
1 code implementation • 3 Dec 2018 • Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar
Can health entities collaboratively train deep learning models without sharing sensitive raw data?
no code implementations • 8 Dec 2018 • Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey
We survey distributed deep learning models for training or inference without accessing raw data from clients.
no code implementations • 14 May 2019 • Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan
We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data.
no code implementations • 5 Oct 2019 • Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders.
no code implementations • 9 Oct 2019 • Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al., 2018b).
no code implementations • 12 Oct 2019 • Tomohiro Maeda, Guy Satat, Tristan Swedish, Lagnojita Sinha, Ramesh Raskar
Seeing around corners, also known as non-line-of-sight (NLOS) imaging is a computational method to resolve or recover objects hidden around corners.
no code implementations • ICCV 2021 • Tristan Swedish, Connor Henley, Ramesh Raskar
We recover high-frequency information encoded in the shadows cast by an object to estimate a hemispherical photograph from the viewpoint of the object, effectively turning objects into cameras.
no code implementations • 21 May 2021 • Subhash Chandra Sadhu, Abhishek Singh, Tomohiro Maeda, Tristan Swedish, Ryan Kim, Lagnojita Sinha, Ramesh Raskar
Time of flight based Non-line-of-sight (NLOS) imaging approaches require precise calibration of illumination and detector positions on the visible scene to produce reasonable results.
no code implementations • ICCV 2023 • Varun Sundar, Andrei Ardelean, Tristan Swedish, Claudio Bruschini, Edoardo Charbon, Mohit Gupta
As an added benefit, our projections provide camera-dependent compression of photon-cubes, which we demonstrate using an implementation of our projections on a novel compute architecture that is designed for single-photon imaging.
no code implementations • ECCV 2020 • Connor Henley, Tomohiro Maeda, Tristan Swedish, Ramesh Raskar
Hidden objects attenuate light that passes through the hidden space, leaving an observable signature that can be used to reconstruct their shape.