598 citations · 2.9k across the 89 of their papers we have counts for
95 papers
Identifying Systematic Errors in Object Detectors with the SCROD Pipeline
Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
The identification and removal of systematic errors in object detectors can be a prerequisite for their deployment in safety-critical applications like automated driving and roboti…
On the Adversarial Robustness of Multi-Modal Foundation Models
Christian Schlarmann, Matthias Hein
Multi-modal foundation models combining vision and language models such as Flamingo or GPT-4 have recently gained enormous interest. Alignment of foundation models is used to preve…
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models
Francesco Croce, Naman D Singh, Matthias Hein
Adversarial robustness has been studied extensively in image classification, especially for the -threat model, but significantly less so for related tasks such as obje…
Normalization Layers Are All That Sharpness-Aware Minimization Needs
Maximilian Mueller, Tiffany Vlaar, David Rolnick +1
Sharpness-aware minimization (SAM) was proposed to reduce sharpness of minima and has been shown to enhance generalization performance in various settings. In this work we show tha…
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Julian Bitterwolf, Maximilian Müller, Matthias Hein
Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to the in-distribution task. The OOD detection performance when the in-distribution (ID…
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
Naman D Singh, Francesco Croce, Matthias Hein
While adversarial training has been extensively studied for ResNet architectures and low resolution datasets like CIFAR, much less is known for ImageNet. Given the recent debate ab…