4 citations · 6 across the 5 of their papers we have counts for
7 papers · 1 filter
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation
Niharika Hegde, Shishir Muralidhara, René Schuster +1
In autonomous driving, environment perception has significantly advanced with the utilization of deep learning techniques for diverse sensors such as cameras, depth sensors, or inf…
AnonyNoise: Anonymizing Event Data with Smart Noise to Outsmart Re-Identification and Preserve Privacy
Katharina Bendig, René Schuster, Nicole Thiemer +2
The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual pr…
CLEO: Continual Learning of Evolving Ontologies
Shishir Muralidhara, Saqib Bukhari, Georg Schneider +2
Continual learning (CL) addresses the problem of catastrophic forgetting in neural networks, which occurs when a trained model tends to overwrite previously learned information, wh…
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support
Ramy Battrawy, René Schuster, Didier Stricker
Recent weakly-supervised methods for scene flow estimation from LiDAR point clouds are limited to explicit reasoning on object-level. These methods perform multiple iterative optim…
RMS-FlowNet++: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds
Ramy Battrawy, René Schuster, Didier Stricker
The proposed RMS-FlowNet++ is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation that can operate on high-density point clouds. For hie…
ShapeAug: Occlusion Augmentation for Event Camera Data
Katharina Bendig, René Schuster, Didier Stricker
Recently, Dynamic Vision Sensors (DVSs) sparked a lot of interest due to their inherent advantages over conventional RGB cameras. These advantages include a low latency, a high dyn…