15 citations · 27 across the 6 of their papers we have counts for
7 papers
Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing
Juliano Pinto, Georg Hess, Yuxuan Xia +2
Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window. Several algorithms have be…
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…
An Uncertainty-Aware Performance Measure for Multi-Object Tracking
Juliano Pinto, Yuxuan Xia, Lennart Svensson +1
Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty informati…
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…
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Wilhelm Tranheden, Viktor Olsson, Juliano Pinto +1
Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically…
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Viktor Olsson, Wilhelm Tranheden, Juliano Pinto +1
The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, p…
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…