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Found 7 papers, 4 papers with code
Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing
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.
Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
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 autonomous driving, tracking animal behavior, defense systems, and others.
An Uncertainty-Aware Performance Measure for Multi-Object Tracking
Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context.
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.
DACS: Domain Adaptation via Cross-domain Mixed Sampling
In this paper we address the problem of unsupervised domain adaptation (UDA), which attempts to train on labelled data from one domain (source domain), and simultaneously learn from unlabelled data in the domain of interest (target domain).
Ranked #14 on
Domain Adaptation
on Cityscapes to ACDC
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation.
Bayesian Linear Regression on Deep Representations
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.
