8 citations · 23 across the 27 of their papers we have counts for
19 papers · 1 filter
Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision
Christoph Hümmer, Joachim Sicking, Fabian Hüger +1
Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In this setting, SAM sets a st…
Extended Field of View Analysis for VideoGAN-based Trajectory Generation
Annajoyce Mariani, Kira Maag, Hanno Gottschalk
Realistic and diverse trajectory generation is central to enabling higher levels of vehicle automation. While rule-based and classical learning-based methods may struggle to captur…
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…
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…
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…
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…
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…
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…
Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning
Christoph Hümmer, Manuel Schwonberg, Liangwei Zhou +3
Domain generalization (DG) remains a significant challenge for perception based on deep neural networks (DNNs), where domain shifts occur due to synthetic data, lighting, weather,…
Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks
Laura Fieback, Bidya Dash, Jakob Spiegelberg +1
Quantifying predictive uncertainty of deep semantic segmentation networks is essential in safety-critical tasks. In applications like autonomous driving, where video data is availa…
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes
Youssef Shoeb, Robin Chan, Gesina Schwalbe +3
In the life cycle of highly automated systems operating in an open and dynamic environment, the ability to adjust to emerging challenges is crucial. For systems integrating data-dr…