265 citations · 361 across the 14 of their papers we have counts for
15 papers
Closed-form Bayesian homography estimation from noisy point correspondences
Hanne Beuter, Sebastian Dorn
While homographies are fundamental to many computer vision tasks, the majority of conventional estimation techniques provide only point estimates without directly quantifying uncer…
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.…
Gradient Derivation for Learnable Parameters in Graph Attention Networks
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powe…
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders
Philipp Joppich, Sebastian Dorn, Oliver De Candido +2
Parametric and non-parametric classifiers often have to deal with real-world data, where corruptions like noise, occlusions, and blur are unavoidable - posing significant challenge…
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding
Yunlei Tang, Sebastian Dorn, Chiragkumar Savani
Localizing objects in 3D space and understanding their associated 3D properties is challenging given only monocular RGB images. The situation is compounded by the loss of depth inf…
A2D2: Audi Autonomous Driving Dataset
Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi +16
Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data. To this end, we release the Audi Autonomous…