no code implementations • 14 Feb 2024 • Edgar Heinert, Matthias Rottmann, Kira Maag, Karsten Kahl
While most of the previous works in the literature focus on the task of image classification, we go beyond this and study the texture bias of CNNs in semantic segmentation.
1 code implementation • 26 Oct 2023 • Kira Maag, Asja Fischer
State-of-the-art deep neural networks have been shown to be extremely powerful in a variety of perceptual tasks like semantic segmentation.
1 code implementation • 22 May 2023 • Kira Maag, Asja Fischer
State-of-the-art deep neural networks have proven to be highly powerful in a broad range of tasks, including semantic image segmentation.
1 code implementation • 13 Mar 2023 • Kira Maag, Tobias Riedlinger
In recent years, deep neural networks have defined the state-of-the-art in semantic segmentation where their predictions are constrained to a predefined set of semantic classes.
1 code implementation • 5 Oct 2022 • Kira Maag, Robin Chan, Svenja Uhlemeyer, Kamil Kowol, Hanno Gottschalk
We present the SOS data set containing 20 video sequences of street scenes and more than 1000 labeled frames with up to two OOD objects.
1 code implementation • 7 Jul 2022 • Kira Maag, Matthias Rottmann
In this work, we enhance semantic segmentation predictions using monocular depth estimation to improve segmentation by reducing the occurrence of non-detected objects in presence of domain shift.
1 code implementation • 28 Jun 2021 • Kira Maag
In our tests, we obtain an improved trade-off between false negative and false positive instances by our fused detection approach in comparison to the use of an ordinary score value provided by the instance segmentation network during inference.
1 code implementation • 14 Dec 2020 • Kira Maag, Matthias Rottmann, Serin Varghese, Fabian Hueger, Peter Schlicht, Hanno Gottschalk
In this paper, we present a time-dynamic approach to model uncertainties of instance segmentation networks and apply this to the detection of false positives as well as the estimation of prediction quality.
no code implementations • 23 Sep 2020 • Matthias Rottmann, Kira Maag, Mathis Peyron, Natasa Krejic, Hanno Gottschalk
In this work we outline a mathematical proof that the CW attack can be used as a detector itself.
1 code implementation • 8 Dec 2019 • Matthias Rottmann, Kira Maag, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk
In recent years, deep learning methods have outperformed other methods in image recognition.
1 code implementation • 12 Nov 2019 • Kira Maag, Matthias Rottmann, Hanno Gottschalk
In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest.