8 citations · 23 across the 27 of their papers we have counts for
12 papers · 1 filter
Domain Generalization for Semantic Segmentation: A Survey
Manuel Schwonberg, Hanno Gottschalk
The generalization of deep neural networks to unknown domains is a major challenge despite their tremendous progress in recent years. For this reason, the dynamic area of domain ge…
Decomposing and Revising What Language Models Generate
Zhichao Yan, Jiaoyan Chen, Jiapu Wang +3
Attribution is crucial in question answering (QA) with Large Language Models (LLMs).SOTA question decomposition-based approaches use long form answers to generate questions for ret…
When do World Models Successfully Learn Dynamical Systems?
Edmund Ross, Claudia Drygala, Leonhard Schwarz +4
In this work, we explore the use of compact latent representations with learned time dynamics ('World Models') to simulate physical systems. Drawing on concepts from control theory…
VideoGAN-based Trajectory Proposal for Automated Vehicles
Annajoyce Mariani, Kira Maag, Hanno Gottschalk
Being able to generate realistic trajectory options is at the core of increasing the degree of automation of road vehicles. While model-driven, rule-based, and classical learning-b…
Learning to Integrate
Oliver G. Ernst, Hanno Gottschalk, Toni Kowalewitz +1
We address uncertainty quantification for a generic input distribution to some resource-intensive simulation, e.g., requiring the solution of a partial differential equation. While…
FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering
Liangyu Zhong, Fabio Rosenthal, Joachim Sicking +4
While Multimodal Large Language Models (MLLMs) offer strong perception and reasoning capabilities for image-text input, Visual Question Answering (VQA) focusing on small image deta…
Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Tobias J. Riedlinger, Kira Maag, Hanno Gottschalk
Deep neural networks have set the state-of-the-art in computer vision tasks such as bounding box detection and semantic segmentation. Object detectors and segmentation models assig…
Generative AI for Autonomous Driving: A Review
Katharina Winter, Abhishek Vivekanandan, Rupert Polley +17
Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how gene…
Learning Brenier Potentials with Convex Generative Adversarial Neural Networks
Claudia Drygala, Hanno Gottschalk, Thomas Kruse +2
Brenier proved that under certain conditions on a source and a target probability measure there exists a strictly convex function such that its gradient is a transport map from the…
Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -
Laura Fieback, Nishilkumar Balar, Jakob Spiegelberg +1
Despite recent advances in Large Vision Language Models (LVLMs), these models still suffer from generating hallucinatory responses that do not align with the visual input provided.…
Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art
Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk
In this paper, we review the state of the art in Out-of-Distribution (OoD) segmentation, with a focus on road obstacle detection in automated driving as a real-world application. W…
Adaptive Neural Networks for Intelligent Data-Driven Development
Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk
Advances in machine learning methods for computer vision tasks have led to their consideration for safety-critical applications like autonomous driving. However, effectively integr…