29 citations · 86 across the 22 of their papers we have counts for
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cs.CV2023★ 13 cited
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation
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. However, these networks are vulnerable t…
cs.CV2023★ 3 cited
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation
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. However, these networks are vulnerable aga…
cs.CV2023★ 8 cited
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation
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. Th…