8 citations · 23 across the 27 of their papers we have counts for
8 papers · 1 filter
Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios
Youssef Shoeb, Nazir Nayal, Azarm Nowzad +2
Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign…
A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models
Manuel Schwonberg, Claus Werner, Hanno Gottschalk +1
Despite the recent progress in deep learning based computer vision, domain shifts are still one of the major challenges. Semantic segmentation for autonomous driving faces a wide r…
Comparison of Generative Learning Methods for Turbulence Surrogates
Claudia Drygala, Edmund Ross, Mohammad Sharifi Ghazijahani +3
Numerical simulations of turbulent flows present significant challenges in fluid dynamics due to their complexity and high computational cost. High resolution techniques such as Di…
New advances in universal approximation with neural networks of minimal width
Dennis Rochau, Robin Chan, Hanno Gottschalk
We prove several universal approximation results at minimal or near-minimal width for approximation of and $C^0(\mathbb{R}^{d_x}, \mathbb{…
On the Influence of Shape, Texture and Color for Learning Semantic Segmentation
Annika Mütze, Natalie Grabowsky, Edgar Heinert +2
Recent research has investigated the shape and texture biases of pre-trained deep neural networks (DNNs) in image classification. Those works test how much a trained DNN relies on…
VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection
Liangyu Zhong, Joachim Sicking, Fabian Hüger +1
Semantic segmentation networks have achieved significant success under the assumption of independent and identically distributed data. However, these networks often struggle to det…
MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification
Laura Fieback, Jakob Spiegelberg, Hanno Gottschalk
Large Vision Language Models (LVLMs) have shown remarkable capabilities in multimodal tasks like visual question answering or image captioning. However, inconsistencies between the…
AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
Daniel Bogdoll, Iramm Hamdard, Lukas Namgyu Rößler +9
The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary t…