Search Results for author: Tristan Swedish

Found 11 papers, 2 papers with code

Imaging Behind Occluders Using Two-Bounce Light

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.

Automatic calibration of time of flight based non-line-of-sight reconstruction

no code implementations21 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.

Objects As Cameras: Estimating High-Frequency Illumination From Shadows

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.

Recent Advances in Imaging Around Corners

no code implementations12 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.

ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations

no code implementations9 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).

Model Selection

Data Markets to support AI for All: Pricing, Valuation and Governance

no code implementations14 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 Peek: A Survey of private distributed deep learning

no code implementations8 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.

Federated Learning

Learning Gaze Transitions From Depth to Improve Video Saliency Estimation

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.

Saliency Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.