4 citations · 6 across the 8 of their papers we have counts for
9 papers
Texture-Shape Bias Balancing for Robust Synthetic-to-Real Semantic Segmentation in Automotive NIR Imagery
Felix Stillger, Ben Hamscher, Lukas Hahn +3
Semantic segmentation is a fundamental component of visual perception in modern automotive systems, enabling pixel-level scene understanding. Near-Infrared imaging (NIR) offers sta…
Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning
Sadia Ilyas, Annika Mütze, Klaus Friedrichs +2
Out-of-distribution (OOD) objects are often silently overlooked by object detectors, which produce no detection for them, leaving downstream modules with nothing to act on and no i…
Decomposing and Revising What Language Models Generate
Zhichao Yan, Jiaoyan Chen, Jiapu Wang +3
Attribution is crucial in question answering (QA) with Large Language Models (LLMs).SOTA question decomposition-based approaches use long form answers to generate questions for ret…
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…
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…
Shape Bias and Robustness Evaluation via Cue Decomposition for Image Classification and Segmentation
Edgar Heinert, Thomas Gottwald, Annika Mütze +1
Previous works studied how deep neural networks (DNNs) perceive image content in terms of their biases towards different image cues, such as texture and shape. Previous methods to…
Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving?
Natalie Grabowsky, Annika Mütze, Joshua Wendland +2
Agents in real-world scenarios like automated driving deal with uncertainty in their environment, in particular due to perceptual uncertainty. Although, reinforcement learning is d…
On the Influence of Shape, Texture and Color for Learning Semantic Segmentation
Annika Mütze, Natalie Grabowsky, Edgar Heinert +2
Recent research has investigated the shape and texture biases of pre-trained deep neural networks (DNNs) in image classification. Those works test how much a trained DNN relies on…
Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss
Annika Mütze, Matthias Rottmann, Hanno Gottschalk
Domain adaptation is of huge interest as labeling is an expensive and error-prone task, especially when labels are needed on pixel-level like in semantic segmentation. Therefore, o…