2 citations · 3 across the 5 of their papers we have counts for
5 papers
Sequence-SOD: Bio-inspired Sequence-aware Spiking ObjectDetection for Event Cameras
Katharina Bendig, René Schuster, Didier Stricker
Event cameras follow a retina-inspired sensing principle, reporting local intensity changes asynchronously with hightemporal resolution and a wide dynamic range. Spiking Neural Net…
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…
ShapeAug++: More Realistic Shape Augmentation for Event Data
Katharina Bendig, René Schuster, Didier Stricker
The novel Dynamic Vision Sensors (DVSs) gained a great amount of attention recently as they are superior compared to RGB cameras in terms of latency, dynamic range and energy consu…
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…
Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences
Katharina Bendig, René Schuster, Didier Stricker
In recent years, deep neural networks showed their exceeding capabilities in addressing many computer vision tasks including scene flow prediction. However, most of the advances ar…