activity
20182022
most citedAn Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions

9 citations · 10 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2022

Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking

Bisheng Wang, Horst Possegger, Horst Bischof +1

Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they stil…

cs.LG2022

Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses

Abhiram Kolli, Muhammad Jehanzeb Mirza, Horst Possegger +1

Keyless entry systems in cars are adopting neural networks for localizing its operators. Using test-time adversarial defences equip such systems with the ability to defend against…

cs.CV2022

SAILOR: Scaling Anchors via Insights into Latent Object Representation

Dušan Malić, Christian Fruhwirth-Reisinger, Horst Possegger +1

LiDAR 3D object detection models are inevitably biased towards their training dataset. The detector clearly exhibits this bias when employed on a target dataset, particularly towar…

cs.CV20229 cited

An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions

M. Jehanzeb Mirza, Marc Masana, Horst Possegger +1

Although deep neural networks enable impressive visual perception performance for autonomous driving, their robustness to varying weather conditions still requires attention. When…

cs.CV20221 cited

OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data

David Schinagl, Georg Krispel, Horst Possegger +2

While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we pr…

cs.CV2022

3D Human Pose Estimation Using Möbius Graph Convolutional Networks

Niloofar Azizi, Horst Possegger, Emanuele Rodolà +1

3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks (GCNs), which achieve state-…