1 citations · 1 across the 11 of their papers we have counts for
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What Is a Clarification? Estimating Model-Dependent Input Ambiguity
Jingqin Tang, Tim Tomov, Sebastian Schmidt +2
Ambiguous inputs can admit multiple interpretations, each potentially yielding a different answer. Existing work largely treats this ambiguity as a property of the input, yet for a…
CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding
Andrea Ceron, Michael Schmidt, Alvaro Marcos-Ramiro +2
Latent LiDAR pipelines suffer from flying pixels: convolutional VAEs blur sharp radial depth discontinuities, yielding edge depths that back-project to points floating between surf…
VOLA: Improving Open-World Driving by VLM-Based Semantic Attribute Prediction
Yuchen Zhang, Yuan Gao, Sebastian Schmidt +1
Driving in the real world is open-world: a car may encounter a fallen mattress, a deer, or other objects outside its training data. Naming them is not enough. The system must know…
Imagined Rollouts are Kinematic, Not Dynamic: A Diagnosis of Long-Horizon World-Model Failure
Finn Rasmus Schäfer, Korbinian Moller, Yuan Gao +3
Long-horizon failure in world models is conventionally attributed to compounding error, a generic framing that does not distinguish what kind of error compounds. We propose a kinem…
Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions
Ha Dang, Sebastian Schmidt, Juergen Hesser
Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently continuous representations struggle…
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Finn Rasmus Schäfer, Yuan Gao, Dingrui Wang +5
While Vision-Language Models (VLMs) have advanced high-level reasoning in autonomous driving, their ability to ground this reasoning in the underlying physics of ego-motion remains…
Amplified Patch-Level Differential Privacy for Free via Random Cropping
Kaan Durmaz, Jan Schuchardt, Sebastian Schmidt +1
Random cropping is one of the most common data augmentation techniques in computer vision, yet the role of its inherent randomness in training differentially private machine learni…