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Found 11 papers, 9 papers with code
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion
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.
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation
State-of-the-art deep neural networks have been shown to be extremely powerful in a variety of perceptual tasks like semantic segmentation.
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation
State-of-the-art deep neural networks have proven to be highly powerful in a broad range of tasks, including semantic image segmentation.
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation
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.
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
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.
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation
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.
False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates
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.
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
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.
Detection of Iterative Adversarial Attacks via Counter Attack
In this work we outline a mathematical proof that the CW attack can be used as a detector itself.
Detection of False Positive and False Negative Samples in Semantic Segmentation
In recent years, deep learning methods have outperformed other methods in image recognition.
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest.
