Co-Author

Foundation Models for Amodal Video Instance Segmentation in Automated Driving
In this work, we study amodal video instance segmentation for automated driving. Previous works perform amodal video instance …
Jasmin Breitenstein, Franz Jünger, Andreas Bär, Tim Fingscheidt
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Non-Causal to Causal SSL-Supported Transfer Learning: Towards A High-Performance Low-Latency Speech Vocoder
Recently, BigVGAN has emerged as high-performance speech vocoder. Its sequence-to-sequence-based synthesis, however, prohibits usage in …
Renzheng Shi, Andreas Bär, Marvin Sach, Wouter Tirry, Tim Fingscheidt
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Detecting Adversarial Perturbations in Multi-Task Perception
While deep neural networks (DNNs) achieve impressive performance on environment perception tasks, their sensitivity to adversarial …
Marvin Klingner, Varun Ravi-Kumar, Senthil Yogamani, Andreas Bär, Tim Fingscheidt
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Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression
We propose a generic approach to quantization without codebook in learned image compression called onehot max (OHM, Ω) quantization. It …
Jonas Löhdefink, Jonas Sitzmann, Andreas Bär, Tim Fingscheidt
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Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation
Although deep neural networks (DNNs) are high-performance methods for various complex tasks, e.g., environment perception in automated …
Atiye Sadat Hashemi, Andreas Bär, Saeed Mozaffari, Tim Fingscheidt
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Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical …
Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle
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Joint Optimization for DNN Model Compression and Corruption Robustness
Modern deep neural networks (DNNs) are achieving state-of-the-art results due to their capability to learn a faithful representation of …
Serin Varghese, Christoph Hümmer, Andreas Bär, Fabian Hüger, Tim Fingscheidt
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From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation
Despite recent advancements, deep neural networks are not robust against adversarial perturbations. Many of the proposed adversarial …
Nikhil Kapoor, Andreas Bär, Serin Varghese, Jan David Schneider, Fabian Hüger, Peter Schlicht, Tim Fingscheidt
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Detection of Collective Anomalies in Images for Automated Driving Using an Earth Mover’s Deviation (EMDEV) Measure
For visual perception in automated driving, a reliable detection of so-called corner cases is important. Corner cases appear in many …
Jasmin Breitenstien, Andreas Bär, Daniel Lipinski, Tim Fingscheidt
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An Unsupervised Temporal Consistency (TC) Loss To Improve the Performance of Semantic Segmentation Networks
Deep neural networks (DNNs) for highly automated driving are often trained on a large and diverse dataset, and evaluation metrics are …
Serin Varghese, Sharat Gujamagadi, Marvin Klingner, Nikhil Kapoor, Andreas Bär, Jan David Schneider, Kira Maag, Peter Schlicht, Fabian Hüger, Tim Fingscheidt
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