8 papers
Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks
Ben Hamscher, Edgar Heinert, Annika Mütze +2
Recent research has investigated the shape and texture biases of deep neural networks (DNNs) in image classification which influence their generalization capabilities and robustnes…
PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage
Thomas Gottwald, Edgar Heinert, Peter Stehr +2
We introduce Primitive-based Representations of Uncertainty (PRIMU), a post-hoc uncertainty estimation (UE) framework for Gaussian Splatting (GS). Reliable UE is essential for depl…
Can We Challenge Open-Vocabulary Object Detectors with Generated Content in Street Scenes?
Annika Mütze, Sadia Ilyas, Christian Dörpelkus +1
Open-vocabulary object detectors such as Grounding DINO are trained on vast and diverse data, achieving remarkable performance on challenging datasets. Due to that, it is unclear w…
Temporal Object Captioning for Street Scene Videos from LiDAR Tracks
Vignesh Gopinathan, Urs Zimmermann, Michael Arnold +1
Video captioning models have seen notable advancements in recent years, especially with regard to their ability to capture temporal information. While many research efforts have fo…
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking
Martha Teiko Teye, Ori Maoz, Matthias Rottmann
Multi-object tracking from LiDAR point clouds presents unique challenges due to the sparse and irregular nature of the data, compounded by the need for temporal coherence across fr…
On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs
Gesina Schwalbe, Georgii Mikriukov, Edgar Heinert +5
The thriving research field of concept-based explainable artificial intelligence (C-XAI) investigates how human-interpretable semantic concepts embed in the latent spaces of deep n…