3 citations · 7 across the 12 of their papers we have counts for
12 papers
CASCADE: A Spatio-Temporal-Causal Reasoning Representation and Dataset for Driving
Jenny Schmalfuss, Despoina Paschalidou, Simon Gerstenecker +2
Reasoning is a promising route to the generalization that autonomous driving requires in the long tail, as it can infer how the elements of a scene depend on one another and traver…
DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models
Xinglong Sun, Kevin Xie, Jenny Schmalfuss +5
Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-d…
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models
Jenny Schmalfuss, Nadine Chang, Vibashan VS +3
Vision language models (VLMs) respond to user-crafted text prompts and visual inputs, and are applied to numerous real-world problems. VLMs integrate visual modalities with large l…
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
Victor Oei, Jenny Schmalfuss, Lukas Mehl +5
Standard benchmarks for optical flow, scene flow, and stereo vision algorithms generally focus on model accuracy rather than robustness to image corruptions like noise or rain. Hen…
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow
Erik Scheurer, Jenny Schmalfuss, Alexander Lis +1
Adversarial patches undermine the reliability of optical flow predictions when placed in arbitrary scene locations. Therefore, they pose a realistic threat to real-world motion det…
Distracting Downpour: Adversarial Weather Attacks for Motion Estimation
Jenny Schmalfuss, Lukas Mehl, Andrés Bruhn
Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, adverse weat…
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
Lukas Mehl, Jenny Schmalfuss, Azin Jahedi +2
While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of ex…
Attacking Motion Estimation with Adversarial Snow
Jenny Schmalfuss, Lukas Mehl, Andrés Bruhn
Current adversarial attacks for motion estimation (optical flow) optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, we exploit a r…
M-FUSE: Multi-frame Fusion for Scene Flow Estimation
Lukas Mehl, Azin Jahedi, Jenny Schmalfuss +1
Recently, neural network for scene flow estimation show impressive results on automotive data such as the KITTI benchmark. However, despite of using sophisticated rigidity assumpti…
Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs
Jenny Schmalfuss, Erik Scheurer, Heng Zhao +3
Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of i…
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
Jenny Schmalfuss, Philipp Scholze, Andrés Bruhn
Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected. Although adversarial attacks offer a useful tool to perfo…
Partitioned Coupling vs. Monolithic Block-Preconditioning Approaches for Solving Stokes-Darcy Systems
Jenny Schmalfuss, Cedric Riethmüller, Mirco Altenbernd +2
We consider the time-dependent Stokes-Darcy problem as a model case for the challenges involved in solving coupled systems. Keeping the model, its discretization, and the underlyin…