12 citations · 36 across the 26 of their papers we have counts for
11 papers · 1 filter
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…
MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds
Georg Krispel, David Schinagl, Christian Fruhwirth-Reisinger +2
The sensing process of large-scale LiDAR point clouds inevitably causes large blind spots, i.e. regions not visible to the sensor. We demonstrate how these inherent sampling proper…
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…
Video Test-Time Adaptation for Action Recognition
Wei Lin, Muhammad Jehanzeb Mirza, Mateusz Kozinski +3
Although action recognition systems can achieve top performance when evaluated on in-distribution test points, they are vulnerable to unanticipated distribution shifts in test data…
ActMAD: Activation Matching to Align Distributions for Test-Time-Training
Muhammad Jehanzeb Mirza, Pol Jané Soneira, Wei Lin +3
Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform…
MATE: Masked Autoencoders are Online 3D Test-Time Learners
M. Jehanzeb Mirza, Inkyu Shin, Wei Lin +8
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring…
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…
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…
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…
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-…
CycDA: Unsupervised Cycle Domain Adaptation from Image to Video
Wei Lin, Anna Kukleva, Kunyang Sun +3
Although action recognition has achieved impressive results over recent years, both collection and annotation of video training data are still time-consuming and cost intensive. Th…