29 citations · 86 across the 22 of their papers we have counts for
4 papers · 1 filter
Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation
Samuel Marschall, Kira Maag
Deep neural networks have shown outstanding performance in computer vision tasks such as semantic segmentation and have defined the state-of-the-art. However, these segmentation mo…
Integrating uncertainty quantification into randomized smoothing based robustness guarantees
Sina Däubener, Kira Maag, David Krueger +1
Deep neural networks have proven to be extremely powerful, however, they are also vulnerable to adversarial attacks which can cause hazardous incorrect predictions in safety-critic…
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis
Kira Maag, Roman Resner, Asja Fischer
Deep neural networks have demonstrated remarkable effectiveness across a wide range of tasks such as semantic segmentation. Nevertheless, these networks are vulnerable to adversari…
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion
Edgar Heinert, Matthias Rottmann, Kira Maag +1
Convolutional neural networks (CNNs) for image processing tend to focus on localized texture patterns, commonly referred to as texture bias. While most of the previous works in the…