297 citations · 1.6k across the 43 of their papers we have counts for
6 papers · 1 filter
Deep Fusion of Multi-Object Densities Using Transformer
Lechi Li, Chen Dai, Yuxuan Xia +1
In this paper, we demonstrate that deep learning based method can be used to fuse multi-object densities. Given a scenario with several sensors with possibly different field-of-vie…
DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
Erik Wallin, Lennart Svensson, Fredrik Kahl +1
Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular. SSL is a family of methods, which in addition to a labeled traini…
Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
Juliano Pinto, Georg Hess, William Ljungbergh +3
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as auto…
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
Juliano Pinto, Georg Hess, William Ljungbergh +3
Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, survei…
Bayesian Linear Regression on Deep Representations
John Moberg, Lennart Svensson, Juliano Pinto +1
A simple approach to obtaining uncertainty-aware neural networks for regression is to do Bayesian linear regression (BLR) on the representation from the last hidden layer. Recent w…
Imitation Learning for Vision-based Lane Keeping Assistance
Christopher Innocenti, Henrik Lindén, Ghazaleh Panahandeh +2
This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a singl…