activity
20192023
most citedGAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation

9 citations · 14 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV2023

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…

cs.CV20231 cited

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…

cs.CV2023

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…

cs.CV2021

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…

cs.CV20205 cited

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…

cs.CV2020

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…