Detection of False Positive and False Negative Samples in Semantic Segmentation. - Matthias Rottmann; Kira Maag; Robin Chan; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (DATE, 2020)

Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. - Kira Maag; Matthias Rottmann; Hanno Gottschalk (ICTAI, 2020)

Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. - Kira Maag; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2020)

An Unsupervised Temporal Consistency (TC) Loss To Improve the Performance of Semantic Segmentation Networks. - Serin Varghese; Sharat Gujamagadi; Marvin Klingner; Nikhil Kapoor; Andreas Bär; Jan David Schneider; Kira Maag; Peter Schlicht; Fabian Hüger; Tim Fingscheidt (CVPR Workshops, 2021)

False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates. - Kira Maag (ICTAI, 2021)

Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. - Kira Maag; Matthias Rottmann; Serin Varghese; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (IJCNN, 2021)

Prediction Rating and Performance Improvement for Segmentation Networks by Time-Dynamic Uncertainty Estimates. - Kira Maag (2021)

Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. - Kira Maag; Robin Chan; Svenja Uhlemeyer; Kamil Kowol; Hanno Gottschalk (ACCV (5), 2022)

False Negative Reduction in Semantic Segmentation Under Domain Shift Using Depth Estimation. - Kira Maag; Matthias Rottmann (VISIGRAPP (5: VISAPP), 2023)

Detection of Iterative Adversarial Attacks via Counter Attack. - Matthias Rottmann; Kira Maag; Mathis Peyron; Hanno Gottschalk; Natasa Krejic (J. Optim. Theory Appl., 2023)

Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion. - Edgar Heinert; Matthias Rottmann; Kira Maag; Karsten Kahl (ECAI, 2024)

Uncertainty-Based Detection of Adversarial Attacks in Semantic Segmentation. - Kira Maag; Asja Fischer (VISIGRAPP (2): VISAPP, 2024)

Pixel-Wise Gradient Uncertainty for Convolutional Neural Networks Applied to Out-of-Distribution Segmentation. - Kira Maag; Tobias Riedlinger (VISIGRAPP (2): VISAPP, 2024)

Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation. - Kira Maag; Asja Fischer (WACV, 2024)

Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis. - Kira Maag; Roman Resner; Asja Fischer (CoRR, 2024)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees. - Sina Däubener; Kira Maag; David Krueger 001; Asja Fischer (CoRR, 2024)

Multi-Scale Foreground-Background Confidence for Out-of-Distribution Segmentation. - Samuel Marschall; Kira Maag (VISIGRAPP (2): VISAPP, 2025)

VideoGAN-based Trajectory Proposal for Automated Vehicles. - Annajoyce Mariani; Kira Maag; Hanno Gottschalk (CoRR, 2025)

Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection. - Tobias Riedlinger; Kira Maag; Hanno Gottschalk (CoRR, 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, 2025)