9 citations · 16 across the 8 of their papers we have counts for
9 papers
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation
Joshua Niemeijer, Manuel Schwonberg, Jan-Aike Termöhlen +2
When models, e.g., for semantic segmentation, are applied to images that are vastly different from training data, the performance will drop significantly. Domain adaptation methods…
Augmentation-based Domain Generalization for Semantic Segmentation
Manuel Schwonberg, Fadoua El Bouazati, Nico M. Schmidt +1
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are two research areas that aim to tackle the lack of generalization of Deep Neural Networks (DNNs) towards unse…
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving
Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen +4
Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play…
PodcastMix: A dataset for separating music and speech in podcasts
Nicolás Schmidt, Jordi Pons, Marius Miron
We introduce PodcastMix, a dataset formalizing the task of separating background music and foreground speech in podcasts. We aim at defining a benchmark suitable for training and e…
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka +8
Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since si…
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs
Nikhil Kapoor, Chun Yuan, Jonas Löhdefink +6
Deep neural networks are often not robust to semantically-irrelevant changes in the input. In this work we address the issue of robustness of state-of-the-art deep convolutional ne…
Self-Supervised Domain Mismatch Estimation for Autonomous Perception
Jonas Löhdefink, Justin Fehrling, Marvin Klingner +4
Autonomous driving requires self awareness of its perception functions. Technically spoken, this can be realized by observers, which monitor the performance indicators of various p…
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks
Felix Assion, Peter Schlicht, Florens Greßner +4
Most state-of-the-art machine learning (ML) classification systems are vulnerable to adversarial perturbations. As a consequence, adversarial robustness poses a significant challen…
GAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation
Jonas Löhdefink, Andreas Bär, Nico M. Schmidt +3
The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems. There is a need to understand tradeoffs between bitr…