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Found 7 papers, 4 papers with code
Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach
Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance.
Medical Physics
Predicting the Driver's Focus of Attention: the DR(eye)VE Project
In this work we aim to predict the driver's focus of attention.
Learning Where to Attend Like a Human Driver
Despite the advent of autonomous cars, it's likely - at least in the near future - that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task.
Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080p, 60fps video taken by 8 cameras observing more than 2, 700 identities over 85 minutes; and (iii) a reference software system as a comparison baseline.
A Statistical Test for Joint Distributions Equivalence
We provide a distribution-free test that can be used to determine whether any two joint distributions $p$ and $q$ are statistically different by inspection of a large enough set of samples.
Learning to Divide and Conquer for Online Multi-Target Tracking
Online Multiple Target Tracking (MTT) is often addressed within the tracking-by-detection paradigm.
Socially Constrained Structural Learning for Groups Detection in Crowd
Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals.
