220 citations · 333 across the 8 of their papers we have counts for
5 papers · 1 filter
Does enhanced shape bias improve neural network robustness to common corruptions?
Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher +3
Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicate…
Grid Saliency for Context Explanations of Semantic Segmentation
Lukas Hoyer, Mauricio Munoz, Prateek Katiyar +2
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limi…
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
Lukas Hoyer, Patrick Kesper, Anna Khoreva +1
An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decisi…
Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation
Jörg Wagner, Volker Fischer, Michael Herman +1
The performance of autonomous systems heavily relies on their ability to generate a robust representation of the environment. Deep neural networks have greatly improved vision-base…
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
Jörg Wagner, Volker Fischer, Michael Herman +1
Generating a robust representation of the environment is a crucial ability of learning agents. Deep learning based methods have greatly improved perception systems but still fail i…