Annika Mütze

According to our database, Annika Mütze authored at least 13 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks.

Ben Hamscher, Edgar Heinert, Annika Mütze, Kira Maag, Matthias Rottmann

CoRR, July, 2025

Can We Challenge Open-Vocabulary Object Detectors with Generated Content in Street Scenes?

Annika Mütze, Sadia Ilyas, Christian Dörpelkus, Matthias Rottmann

CoRR, June, 2025

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks.

Claudia Drygala, Hanno Gottschalk, Thomas Kruse, Ségolène Martin, Annika Mütze

CoRR, April, 2025

On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs.

Gesina Schwalbe, Georgii Mikriukov, Edgar Heinert, Stavros Gerolymatos, Mert Keser, Alois Knoll, Matthias Rottmann, Annika Mütze

CoRR, April, 2025

Shape Bias and Robustness Evaluation via Cue Decomposition for Image Classification and Segmentation.

Edgar Heinert, Thomas Gottwald, Annika Mütze, Matthias Rottmann

CoRR, March, 2025

Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving?

Natalie Grabowsky, Annika Mütze, Joshua Wendland, Nils Jansen, Matthias Rottmann

CoRR, February, 2025

2024

Visual Recognition Using Deep Neural Networks on Abstract and Decomposed Data.

Annika Mütze

PhD thesis, 2024

On the Influence of Shape, Texture and Color for Learning Semantic Segmentation.

Annika Mütze, Natalie Grabowsky, Edgar Heinert, Matthias Rottmann, Hanno Gottschalk

CoRR, 2024

AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains.

Krzysztof Baron-Lis, Matthias Rottmann, Annika Mütze, Sina Honari, Pascal Fua, Mathieu Salzmann

Proceedings of the 35th British Machine Vision Conference, 2024

2023

Semi-Supervised Domain Adaptation with CycleGAN Guided by Downstream Task Awareness.

Annika Mütze, Matthias Rottmann, Hanno Gottschalk

Proceedings of the 18th International Joint Conference on Computer Vision, 2023

2022

Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss.

Annika Mütze, Matthias Rottmann, Hanno Gottschalk

CoRR, 2022

Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning.

Julian Burghoff, Robin Chan, Hanno Gottschalk, Annika Mütze, Tobias Riedlinger, Matthias Rottmann, Marius Schubert

CoRR, 2022

2013

SPIN as a Linearizability Checker under Weak Memory Models.

Oleg Travkin, Annika Mütze, Heike Wehrheim

Proceedings of the Hardware and Software: Verification and Testing, 2013