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Bernhard Sick
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- affiliation: University of Kassel, Germany
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2020 – today
- 2024
- [j52]Denis Huseljic, Marek Herde, Yannick Nagel, Lukas Rauch, Paulius Strimaitis, Bernhard Sick:
The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification. Trans. Mach. Learn. Res. 2024 (2024) - [c175]Jens Decke, Arne Jenß, Bernhard Sick, Christian Gruhl:
An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing. ARCS 2024: 51-66 - [c174]Jens Decke, Olaf Wünsch, Bernhard Sick, Christian Gruhl:
From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in Solving Mesh-Based PDEs. ARCS 2024: 82-96 - [c173]Hannes Reichert, Manuel Hetzel, Andreas Hubert, Konrad Doll, Bernhard Sick:
Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models. CVPR Workshops 2024: 1254-1261 - [c172]Marek Herde, Lukas Lührs, Denis Huseljic, Bernhard Sick:
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension. ECAI 2024: 2910-2918 - [c171]Marek Herde, Tuan Pham Minh, Alaa Tharwat, Bernhard Sick:
Tutorial: Interactive Adaptive Learning. IAL@PKDD/ECML 2024: 1-6 - [c170]Lukas Rauch, Denis Huseljic, Moritz Wirth, Jens Decke, Bernhard Sick, Christoph Scholz:
Towards Deep Active Learning in Avian Bioacoustics. IAL@PKDD/ECML 2024: 12-17 - [c169]Paul Hahn, Denis Huseljic, Marek Herde, Bernhard Sick:
General Reusability: Ensuring Long-Term Benefits of Deep Active Learning. IAL@PKDD/ECML 2024: 33-46 - [c168]Jens Decke, Alexander Heinen, Bernhard Sick, Christian Gruhl:
Active Learning with Physics-Informed Graph Neural Networks on Unstructured Meshes. IAL@PKDD/ECML 2024: 68-76 - [c167]Zhixin Huang, Yujiang He, Chandana Priya Nivarthi, Christian Gruhl, Bernhard Sick:
Spatial-Temporal Attention Graph Neural Network with Uncertainty Estimation for Remaining Useful Life Prediction. IJCNN 2024: 1-9 - [c166]Chandana Priya Nivarthi, Zhixin Huang, Christian Gruhl, Bernhard Sick:
Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection. IJCNN 2024: 1-10 - [c165]Denis Huseljic, Paul Hahn, Marek Herde, Lukas Rauch, Bernhard Sick:
Fast Fishing: Approximating Bait for Efficient and Scalable Deep Active Image Classification. ECML/PKDD (7) 2024: 280-296 - [c164]Diego Botache, Jens Decke, Winfried Ripken, Abhinay Dornipati, Franz Götz-Hahn, Mohamed Ayeb, Bernhard Sick:
Enhancing Multi-objective Optimisation Through Machine Learning-Supported Multiphysics Simulation. ECML/PKDD (10) 2024: 297-312 - [i80]Zhixin Huang, Yujiang He, Bernhard Sick:
Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction. CoRR abs/2401.15964 (2024) - [i79]Lukas Rauch, Raphael Schwinger, Moritz Wirth, René Heinrich, Jonas Lange, Stefan Kahl, Bernhard Sick, Sven Tomforde, Christoph Scholz:
BirdSet: A Multi-Task Benchmark for Classification in Avian Bioacoustics. CoRR abs/2403.10380 (2024) - [i78]Denis Huseljic, Paul Hahn, Marek Herde, Lukas Rauch, Bernhard Sick:
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification. CoRR abs/2404.08981 (2024) - [i77]René Heinrich, Bernhard Sick, Christoph Scholz:
AudioProtoPNet: An interpretable deep learning model for bird sound classification. CoRR abs/2404.10420 (2024) - [i76]Florian Heidecker, Ahmad El-Khateeb, Maarten Bieshaar, Bernhard Sick:
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation. CoRR abs/2404.11266 (2024) - [i75]Marek Herde, Lukas Lührs, Denis Huseljic, Bernhard Sick:
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension. CoRR abs/2405.03386 (2024) - [i74]Jens Decke, Arne Jenß, Bernhard Sick, Christian Gruhl:
An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing. CoRR abs/2406.00080 (2024) - [i73]Jens Decke, Olaf Wünsch, Bernhard Sick, Christian Gruhl:
From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs. CoRR abs/2406.00081 (2024) - [i72]Lukas Rauch, Denis Huseljic, Moritz Wirth, Jens Decke, Bernhard Sick, Christoph Scholz:
Towards Deep Active Learning in Avian Bioacoustics. CoRR abs/2406.18621 (2024) - [i71]Martin Braun, Christian Gruhl, Christian A. Hans, Philipp Härtel, Christoph Scholz, Bernhard Sick, Malte Siefert, Florian Steinke, Olaf Stursberg, Sebastian Wende-von Berg:
Predictions and Decision Making for Resilient Intelligent Sustainable Energy Systems. CoRR abs/2407.03021 (2024) - [i70]Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev, Josephine M. Thomas, Bernhard Sick, Christoph Scholz:
Graph Reinforcement Learning in Power Grids: A Survey. CoRR abs/2407.04522 (2024) - [i69]Marek Herde, Denis Huseljic, Lukas Rauch, Bernhard Sick:
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans. CoRR abs/2407.20950 (2024) - [i68]Mohammad Wazed Ali, Asif bin Mustafa, Md. Aukerul Moin Shuvo, Bernhard Sick:
Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network. CoRR abs/2409.11862 (2024) - 2023
- [j51]Chandana Priya Nivarthi, Stephan Vogt, Bernhard Sick:
Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions. Mach. Learn. Knowl. Extr. 5(3): 1214-1233 (2023) - [j50]Markus Eider, Bernhard Sick, Andreas Berl:
Context-aware recommendations for extended electric vehicle battery lifetime. Sustain. Comput. Informatics Syst. 37: 100845 (2023) - [j49]Viktor Kress, Fabian Jeske, Stefan Zernetsch, Konrad Doll, Bernhard Sick:
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories. IEEE Trans. Intell. Veh. 8(3): 2592-2603 (2023) - [j48]Marek Herde, Denis Huseljic, Bernhard Sick:
Multi-annotator Deep Learning: A Probabilistic Framework for Classification. Trans. Mach. Learn. Res. 2023 (2023) - [c163]Christian Gruhl, Bernhard Sick:
Self- Integration and Agent Compatibility. ACSOS-C 2023: 71-73 - [c162]Birk Martin Magnussen, Claudius Stern, Bernhard Sick:
Leveraging Repeated Unlabelled Noisy Measurements to Augment Supervised Learning. CIIS 2023: 1-6 - [c161]Christoph Sandrock, Marek Herde, Daniel Kottke, Bernhard Sick:
Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy. DS 2023: 265-276 - [c160]Marek Herde, Denis Huseljic, Bernhard Sick, Ulrich Bretschneider, Sarah Oeste-Reiß:
Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning. IAL@PKDD/ECML 2023: 14-18 - [c159]Denis Huseljic, Marek Herde, Paul Hahn, Bernhard Sick:
Role of Hyperparameters in Deep Active Learning. IAL@PKDD/ECML 2023: 19-24 - [c158]Zhixin Huang, Yujiang He, Marek Herde, Denis Huseljic, Bernhard Sick:
Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets. IAL@PKDD/ECML 2023: 25-45 - [c157]Matthias Aßenmacher, Lukas Rauch, Jann Goschenhofer, Andreas Stephan, Bernd Bischl, Benjamin Roth, Bernhard Sick:
Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. IAL@PKDD/ECML 2023: 65-73 - [c156]Steven Schreck, Hannes Reichert, Manuel Hetzel, Konrad Doll, Bernhard Sick:
Height Change Feature Based Free Space Detection. ICCMA 2023: 171-176 - [c155]Jasmin Breitenstein, Florian Heidecker, Maria Lyssenko, Daniel Bogdoll, Maarten Bieshaar, J. Marius Zöllner, Bernhard Sick, Tim Fingscheidt:
What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving. ICCV (Workshops) 2023: 3993-4002 - [c154]Chandana Priya Nivarthi, Bernhard Sick:
Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding. ICMLA 2023: 1444-1450 - [c153]Jens Decke, Christian Gruhl, Lukas Rauch, Bernhard Sick:
DADO - Low-Cost Query Strategies for Deep Active Design Optimization. ICMLA 2023: 1611-1618 - [c152]Florian Heidecker, Tobias Susetzky, Erich Fuchs, Bernhard Sick:
Context Information for Corner Case Detection in Highly Automated Driving. ITSC 2023: 1522-1529 - [c151]Manuel Hetzel, Hannes Reichert, Günther Reitberger, Erich Fuchs, Konrad Doll, Bernhard Sick:
The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset. IV 2023: 1-7 - [c150]Hannes Reichert, Manuel Hetzel, Steven Schreck, Konrad Doll, Bernhard Sick:
Sensor Equivariance by LiDAR Projection Images. IV 2023: 1-6 - [c149]Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick:
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. ECML/PKDD (1) 2023: 55-74 - [i67]Marek Herde, Denis Huseljic, Bernhard Sick:
Multi-annotator Deep Learning: A Probabilistic Framework for Classification. CoRR abs/2304.02539 (2023) - [i66]Hannes Reichert, Manuel Hetzel, Steven Schreck, Konrad Doll, Bernhard Sick:
Sensor Equivariance by LiDAR Projection Images. CoRR abs/2305.00221 (2023) - [i65]Jens Decke, Olaf Wünsch, Bernhard Sick:
Dataset of a parameterized U-bend flow for Deep Learning Applications. CoRR abs/2305.05216 (2023) - [i64]Florian Heidecker, Ahmad El-Khateeb, Bernhard Sick:
Sampling-based Uncertainty Estimation for an Instance Segmentation Network. CoRR abs/2305.14977 (2023) - [i63]Lukas Rauch, Matthias Aßenmacher, Denis Huseljic, Moritz Wirth, Bernd Bischl, Bernhard Sick:
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. CoRR abs/2306.10087 (2023) - [i62]Jens Decke, Christian Gruhl, Lukas Rauch, Bernhard Sick:
DADO - Low-Cost Selection Strategies for Deep Active Design Optimization. CoRR abs/2307.04536 (2023) - [i61]Manuel Hetzel, Hannes Reichert, Günther Reitberger, Erich Fuchs, Konrad Doll, Bernhard Sick:
The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset. CoRR abs/2307.06165 (2023) - [i60]Manuel Hetzel, Hannes Reichert, Konrad Doll, Bernhard Sick:
Smart Infrastructure: A Research Junction. CoRR abs/2307.06177 (2023) - [i59]Diego Botache, Kristina Dingel, Rico Huhnstock, Arno Ehresmann, Bernhard Sick:
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis. CoRR abs/2307.14294 (2023) - [i58]Steven Schreck, Hannes Reichert, Manuel Hetzel, Konrad Doll, Bernhard Sick:
Height Change Feature Based Free Space Detection. CoRR abs/2308.00971 (2023) - [i57]Lukas Rauch, Raphael Schwinger, Moritz Wirth, Bernhard Sick, Sven Tomforde, Christoph Scholz:
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers. CoRR abs/2308.07121 (2023) - [i56]Tuan Pham Minh, Jayan Wijesingha, Daniel Kottke, Marek Herde, Denis Huseljic, Bernhard Sick, Michael Wachendorf, Thomas Esch:
Active Label Refinement for Semantic Segmentation of Satellite Images. CoRR abs/2309.06159 (2023) - [i55]Diego Botache, Jens Decke, Winfried Ripken, Abhinay Dornipati, Franz Götz-Hahn, Mohamed Ayeb, Bernhard Sick:
Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation. CoRR abs/2309.13179 (2023) - 2022
- [j47]Christian Krupitzer, Christian Gruhl, Bernhard Sick, Sven Tomforde:
Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario. Inf. Softw. Technol. 145: 106826 (2022) - [j46]Claude Draude, Christian Gruhl, Gerrit Hornung, Jonathan Kropf, Jörn Lamla, Jan Marco Leimeister, Bernhard Sick, Gerd Stumme:
Social Machines. Inform. Spektrum 45(1): 38-42 (2022) - [j45]Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm:
Efficient SVDD sampling with approximation guarantees for the decision boundary. Mach. Learn. 111(4): 1349-1375 (2022) - [j44]Tuan Pham, Daniel Kottke, Georg Krempl, Bernhard Sick:
Stream-based active learning for sliding windows under the influence of verification latency. Mach. Learn. 111(6): 2011-2036 (2022) - [c148]Christian Gruhl, Sven Tomforde, Bernhard Sick:
Self-Aware Microsystems. ACSOS-C 2022: 126-127 - [c147]Jens Decke, Jörn Schmeißing, Diego Botache, Maarten Bieshaar, Bernhard Sick, Christian Gruhl:
NDNET: A Unified Framework for Anomaly and Novelty Detection. ARCS 2022: 197-210 - [c146]Tobias Westmeier, Diego Botache, Maarten Bieshaar, Bernhard Sick:
Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches. DSAA 2022: 1-10 - [c145]Chandana Priya Nivarthi, Stephan Vogt, Bernhard Sick:
Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting. ICMLA 2022: 1530-1536 - [c144]Stefan Zernetsch, Hannes Reichert, Viktor Kress, Konrad Doll, Bernhard Sick:
A Holistic View on Probabilistic Trajectory Forecasting - Case Study. Cyclist Intention Detection. IV 2022: 265-272 - [c143]Marek Herde, Denis Huseljic, Jelena Mitrovic, Michael Granitzer, Bernhard Sick:
A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning. IAL@PKDD/ECML 2022: 1-6 - [c142]Lukas Rauch, Denis Huseljic, Bernhard Sick:
Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources. IAL@PKDD/ECML 2022: 27-42 - [c141]Jan Schneegans, Maarten Bieshaar, Bernhard Sick:
A Practical Evaluation of Active Learning Approaches for Object Detection. IAL@PKDD/ECML 2022: 49-67 - [c140]Daniel Kottke, Christoph Sandrock, Georg Krempl, Bernhard Sick:
A Stopping Criterion for Transductive Active Learning. ECML/PKDD (4) 2022: 468-484 - [c139]Kevin Rösch, Florian Heidecker, Julian Truetsch, Kamil Kowol, Clemens Schicktanz, Maarten Bieshaar, Bernhard Sick, Christoph Stiller:
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving. SSCI 2022: 86-93 - [i54]Yujiang He, Zhixin Huang, Bernhard Sick:
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning. CoRR abs/2202.06781 (2022) - [i53]Stephan Vogt, Jens Schreiber, Bernhard Sick:
Synthetic Photovoltaic and Wind Power Forecasting Data. CoRR abs/2204.00411 (2022) - [i52]Jens Schreiber, Bernhard Sick:
Model Selection, Adaptation, and Combination for Deep Transfer Learning through Neural Networks in Renewable Energies. CoRR abs/2204.13293 (2022) - [i51]Jens Schreiber, Stephan Vogt, Bernhard Sick:
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast. CoRR abs/2204.13908 (2022) - [i50]Denis Huseljic, Marek Herde, Mehmet Muejde, Bernhard Sick:
A Review of Uncertainty Calibration in Pretrained Object Detectors. CoRR abs/2210.02935 (2022) - [i49]Marek Herde, Zhixin Huang, Denis Huseljic, Daniel Kottke, Stephan Vogt, Bernhard Sick:
Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning. CoRR abs/2210.06112 (2022) - [i48]Kevin Rösch, Florian Heidecker, Julian Truetsch, Kamil Kowol, Clemens Schicktanz, Maarten Bieshaar, Bernhard Sick, Christoph Stiller:
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving. CoRR abs/2210.08885 (2022) - 2021
- [j43]Marek Herde, Denis Huseljic, Bernhard Sick, Adrian Calma:
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification. IEEE Access 9: 166970-166989 (2021) - [j42]Kristina Dingel, Rico Huhnstock, André Knie, Arno Ehresmann, Bernhard Sick:
AdaPT: Adaptable Particle Tracking for spherical microparticles in lab on chip systems. Comput. Phys. Commun. 262: 107859 (2021) - [j41]Christian Gruhl, Bernhard Sick, Sven Tomforde:
Novelty detection in continuously changing environments. Future Gener. Comput. Syst. 114: 138-154 (2021) - [j40]Sarah Oeste-Reiß, Eva A. C. Bittner, Izabel Cvetkovic, Andreas Günther, Jan Marco Leimeister, Lucas Memmert, Anja Ott, Bernhard Sick, Kathrin Wolter:
Hybride Wissensarbeit. Inform. Spektrum 44(3): 148-152 (2021) - [j39]Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl, Bernhard Sick:
Toward optimal probabilistic active learning using a Bayesian approach. Mach. Learn. 110(6): 1199-1231 (2021) - [c138]Diego Botache, Florian Bethke, Martin Hardieck, Maarten Bieshaar, Ludwig Brabetz, Mohamed Ayeb, Peter Zipf, Bernhard Sick:
Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands. ACSOS 2021: 120-130 - [c137]Kristina Dingel, A. Liehr, M. Vogel, S. Degener, David Meier, Thoralf Niendorf, Arno Ehresmann, Bernhard Sick:
AI - Based On The Fly Design of Experiments in Physics and Engineering. ACSOS-C 2021: 150-153 - [c136]Abdul Hannan, Christian Gruhl, Bernhard Sick:
Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders. CSR 2021: 1-7 - [c135]Felix Möller, Diego Botache, Denis Huseljic, Florian Heidecker, Maarten Bieshaar, Bernhard Sick:
Out-of-Distribution Detection and Generation Using Soft Brownian Offset Sampling and Autoencoders. CVPR Workshops 2021: 46-55 - [c134]Matthias Reuse, Martin Simon, Bernhard Sick:
About the Ambiguity of Data Augmentation for 3D Object Detection in Autonomous Driving. ICCVW 2021: 979-987 - [c133]Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker, Maarten Bieshaar, Bernhard Sick, Tim Fingscheidt, J. Marius Zöllner:
Description of Corner Cases in Automated Driving: Goals and Challenges. ICCVW 2021: 1023-1028 - [c132]Yujiang He, Zhixin Huang, Bernhard Sick:
Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. IJCNN 2021: 1-8 - [c131]Manuel Hetzel, Hannes Reichert, Konrad Doll, Bernhard Sick:
Smart Infrastructure: A Research Junction. ISC2 2021: 1-4 - [c130]Hannes Reichert, Lukas Lang, Kevin Rösch, Daniel Bogdoll, Konrad Doll, Bernhard Sick, Hans-Christian Reuss, Christoph Stiller, J. Marius Zöllner:
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. ISC2 2021: 1-4 - [c129]Stefan Zernetsch, Oliver Trupp, Viktor Kress, Konrad Doll, Bernhard Sick:
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information. ISC2 2021: 1-7 - [c128]Jan Schneegans, Jan Eilbrecht, Stefan Zernetsch, Maarten Bieshaar, Konrad Doll, Olaf Stursberg, Bernhard Sick:
Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists. IV Workshops 2021: 272-279 - [c127]Florian Heidecker, Jasmin Breitenstein, Kevin Rösch, Jonas Löhdefink, Maarten Bieshaar, Christoph Stiller, Tim Fingscheidt, Bernhard Sick:
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving. IV 2021: 644-651 - [c126]Zhixin Huang, Yujiang He, Stephan Vogt, Bernhard Sick:
Uncertainty and Utility Sampling with Pre-Clustering. IAL@PKDD/ECML 2021: 21-34 - [c125]Maarten Bieshaar, Marek Herde, Denis Huseljic, Bernhard Sick:
A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving. IAL@PKDD/ECML 2021: 65-69 - [c124]Jens Schreiber, Stephan Vogt, Bernhard Sick:
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast. ECML/PKDD (4) 2021: 118-134 - [c123]Maarten Bieshaar, Stefan Zernetsch, Katharina Riepe, Konrad Doll, Bernhard Sick:
Cyclist Motion State Forecasting - Going beyond Detection. SSCI 2021: 1-8 - [d2]Viktor Kress, Stefan Zernetsch, Maarten Bieshaar, Günther Reitberger, Erich Fuchs, Konrad Doll, Bernhard Sick:
Pedestrians and Cyclists in Road Traffic: Trajectories, 3D Poses and Semantic Maps. Zenodo, 2021 - [d1]Viktor Kress, Stefan Zernetsch, Hannes Reichert, Manuel Hetzel, Maarten Bieshaar, Günther Reitberger, Erich Fuchs, Konrad Doll, Bernhard Sick:
Aschaffenburg Pose Dataset. Zenodo, 2021 - [i47]Yujiang He, Bernhard Sick:
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks. CoRR abs/2101.00926 (2021) - [i46]Florian Heidecker, Jasmin Breitenstein, Kevin Rösch, Jonas Löhdefink, Maarten Bieshaar, Christoph Stiller, Tim Fingscheidt, Bernhard Sick:
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving. CoRR abs/2103.03678 (2021) - [i45]Stefan Zernetsch, Hannes Reichert, Viktor Kress, Konrad Doll, Bernhard Sick:
Cyclist Intention Detection: A Probabilistic Approach. CoRR abs/2104.09176 (2021) - [i44]Felix Möller, Diego Botache, Denis Huseljic, Florian Heidecker, Maarten Bieshaar, Bernhard Sick:
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders. CoRR abs/2105.02965 (2021) - [i43]Hannes Reichert, Lukas Lang, Kevin Rösch, Daniel Bogdoll, Konrad Doll, Bernhard Sick, Hans-Christian Reuss, Christoph Stiller, J. Marius Zöllner:
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. CoRR abs/2105.06896 (2021) - [i42]Viktor Kress, Fabian Jeske, Stefan Zernetsch, Konrad Doll, Bernhard Sick:
Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories. CoRR abs/2106.02598 (2021) - [i41]Stefan Zernetsch, Oliver Trupp, Viktor Kress, Konrad Doll, Bernhard Sick:
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information. CoRR abs/2106.15991 (2021) - [i40]Daniel Kottke, Georg Krempl, Marianne Stecklina, Cornelius Styp von Rekowski, Tim Sabsch, Tuan Pham Minh, Matthias Deliano, Myra Spiliopoulou, Bernhard Sick:
Probabilistic Active Learning for Active Class Selection. CoRR abs/2108.03891 (2021) - [i39]Kristina Dingel, Thorsten Otto, Lutz Marder, Lars Funke, Arne Held, Sara Savio, Andreas Hans, Gregor Hartmann, David Meier, Jens Viefhaus, Bernhard Sick, Arno Ehresmann, Markus Ilchen, Wolfram Helml:
Toward AI-enhanced online-characterization and shaping of ultrashort X-ray free-electron laser pulses. CoRR abs/2108.13979 (2021) - [i38]Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker, Maarten Bieshaar, Bernhard Sick, Tim Fingscheidt, J. Marius Zöllner:
Description of Corner Cases in Automated Driving: Goals and Challenges. CoRR abs/2109.09607 (2021) - [i37]