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Found 12 papers, 2 papers with code
Imaging Behind Occluders Using Two-Bounce Light
Hidden objects attenuate light that passes through the hidden space, leaving an observable signature that can be used to reconstruct their shape.
SoDaCam: Software-defined Cameras via Single-Photon Imaging
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.
Automatic calibration of time of flight based non-line-of-sight reconstruction
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
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
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
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).
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders.
Data Markets to support AI for All: Pricing, Valuation and Governance
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
We survey distributed deep learning models for training or inference without accessing raw data from clients.
Split learning for health: Distributed deep learning without sharing raw patient data
Can health entities collaboratively train deep learning models without sharing sensitive raw data?
3D Traffic Simulation for Autonomous Vehicles in Unity and Python
Over the recent years, there has been an explosion of studies on autonomous vehicles.
Learning Gaze Transitions From Depth to Improve Video Saliency Estimation
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.
