49 citations · 63 across the 15 of their papers we have counts for
17 papers
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation
Hariprasath Govindarajan, Maciej K. Wozniak, Marvin Klingner +3
Vision foundation models (VFMs) such as DINO have led to a paradigm shift in 2D camera-based perception towards extracting generalized features to support many downstream tasks. Re…
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
Maciej K. Wozniak, Hariprasath Govindarajan, Marvin Klingner +3
Recent self-supervised clustering-based pre-training techniques like DINO and Cribo have shown impressive results for downstream detection and segmentation tasks. However, real-wor…
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Shubhankar Borse, Senthil Yogamani, Marvin Klingner +4
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
XKD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar +4
Recent advances in 3D object detection (3DOD) have obtained remarkably strong results for LiDAR-based models. In contrast, surround-view 3DOD models based on multiple camera images…
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Shubhankar Borse, Marvin Klingner, Varun Ravi Kumar +4
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models
Marvin Klingner, Konstantin Müller, Mona Mirzaie +3
The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving. While much effort is put into im…
Detecting Adversarial Perturbations in Multi-Task Perception
Marvin Klingner, Varun Ravi Kumar, Senthil Yogamani +2
While deep neural networks (DNNs) achieve impressive performance on environment perception tasks, their sensitivity to adversarial perturbations limits their use in practical appli…
Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation
Marvin Klingner, Mouadh Ayache, Tim Fingscheidt
Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased…
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These s…
Improving Online Performance Prediction for Semantic Segmentation
Marvin Klingner, Andreas Bär, Marcel Mross +1
In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i.e., during inference, which is of high…
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
Varun Ravi Kumar, Marvin Klingner, Senthil Yogamani +4
A 360° perception of scene geometry is essential for automated driving, notably for parking and urban driving scenarios. Typically, it is achieved using surround-view fisheye camer…
Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representations
Marvin Klingner, Jan-Aike Termöhlen, Jacob Ritterbach +1
In this paper we present a solution to the task of "unsupervised domain adaptation (UDA) of a given pre-trained semantic segmentation model without relying on any source domain rep…
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving
Varun Ravi Kumar, Marvin Klingner, Senthil Yogamani +3
State-of-the-art self-supervised learning approaches for monocular depth estimation usually suffer from scale ambiguity. They do not generalize well when applied on distance estima…
Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance
Marvin Klingner, Jan-Aike Termöhlen, Jonas Mikolajczyk +1
Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene information from single camera images, which is trainable on arbitrary image sequences with…
Self-Supervised Domain Mismatch Estimation for Autonomous Perception
Jonas Löhdefink, Justin Fehrling, Marvin Klingner +4
Autonomous driving requires self awareness of its perception functions. Technically spoken, this can be realized by observers, which monitor the performance indicators of various p…
Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels
Marvin Klingner, Andreas Bär, Philipp Donn +1
While neural networks trained for semantic segmentation are essential for perception in autonomous driving, most current algorithms assume a fixed number of classes, presenting a m…
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation
Marvin Klingner, Andreas Bär, Tim Fingscheidt
While current approaches for neural network training often aim at improving performance, less focus is put on training methods aiming at robustness towards varying noise conditions…