Fingas, M.[Merv]
* 2018: Challenges of Remotely Measuring Oil Slick Thickness, The
Finger, D.C.[David Christian]
* 2021: Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland
Fingerle, A.[Alexander]
* 2022: Correction of Motion Artifacts in Dark-Field Radiography of the Human Chest
* 2024: Correction for Mechanical Inaccuracies in a Scanning Talbot-Lau Interferometer
Fingerman, S.[Samuel]
* 2019: Concept Development and Risk Reduction for MISTiC Winds, A Micro-Satellite Constellation Approach for Vertically Resolved Wind and IR Sounding Observations in the Troposphere
Fingscheidt, T.[Tim]
* 2012: Training of Classifiers for Quality Control of On-Line Laser Brazing Processes with Highly Imbalanced Datasets
* 2013: On Evaluation of Segmentation-Free Word Spotting Approaches without Hard Decisions
* 2014: Writer Identification for Historical Arabic Documents
* 2020: Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation
* 2020: Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote
* 2020: Self-Supervised Domain Mismatch Estimation for Autonomous Perception
* 2020: Self-supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance
* 2020: Unsupervised Temporal Consistency Metric for Video Segmentation in Highly-Automated Driving
* 2021: Description of Corner Cases in Automated Driving: Goals and Challenges
* 2021: Improving Online Performance Prediction for Semantic Segmentation
* 2021: Online Performance Prediction of Perception DNNs by Multi-Task Learning With Depth Estimation
* 2021: SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving
* 2021: Unsupervised Temporal Consistency (TC) Loss to Improve the Performance of Semantic Segmentation Networks, An
* 2021: Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing, The
* 2022: Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression
* 2022: Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation
* 2022: Joint Prediction of Amodal and Visible Semantic Segmentation for Automated Driving
* 2022: On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models
* 2022: Performance Prediction for Semantic Segmentation by a Self-Supervised Image Reconstruction Decoder
* 2022: SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
* 2022: Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representations
* 2023: Improvements to Image Reconstruction-Based Performance Prediction for Semantic Segmentation in Highly Automated Driving
* 2023: Novel Benchmark for Refinement of Noisy Localization Labels in Autolabeled Datasets for Object Detection, A
* 2023: Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation, A
* 2023: Super-Resolution Training Paradigm Based on Low-Resolution Data Only to Surpass the Technical Limits of STEM and STM Microscopy, A
* 2023: What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving
* 2024: Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation
Includes: Fingscheidt, T.[Tim] Fingscheidt, T.
27 for Fingscheidt, T.