Andreas Bär
PhD Student @
Technische Universität Braunschweig
Biography
I am a final-year PhD student in Electrical Engineering under supervision of Tim Fingscheidt, Professor at the Institute for Communications Technology, Technische Universität Braunschweig. During my PhD I focused on computer vision, in particular:
- Scene understanding
- Out-of-distribution robustness, with an emphasis on adversarial attacks
- Teacher-student learning
- Performance prediction
Besides that I am also interested in:
- Vision foundation models
- (Multimodal) large language models
- Training at scale
- Efficient transfer learning
Education
-
PhD in Electrical Engineering, 2025 (expected)
Technische Universität Braunschweig, Braunschweig, Germany
-
MSc in Electrical Engineering, 2018
Technische Universität Braunschweig, Braunschweig, Germany
-
BEng in Electrical Engineering, 2016
Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
Awards
Best Paper Award 🏆
Serin Varghese, Sharat Gujamagadi, Marvin Klingner, Nikhil Kapoor, Andreas Bär, Jonas Schneider, Kira Maag, Peter Schlicht, Fabian Hüger, and Tim Fingscheidt. An Unsupervised Temporal Consistency (TC) Loss to Improve the Performance of Semantic Segmentation Networks (2021). In Proc. of CVPR - Workshops.
Best Paper Award 🏆
Andreas Bär, Marvin Klingner, Serin Varghese, Fabian Hüger, Peter Schlicht, and Tim Fingscheidt. Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote (2020). In Proc. of CVPR - Workshops.
Work Experience
Research Associate
- Internal project lead in research projects, e.g., KI Absicherung
- Write and share research code in Python, e.g., TUBSRobustCheck, PerfPredRec, …
- Write and publish scientific papers (Google scholar)
- Supervise student bachelor and master theses as well as student research assistants
- Peer review for conferences and journals, e.g., IEEE/CVF CVPR, ICRA, IROS, IJCV, IEEE RA-L, Neurocomputing, Pattern Recognition, …
Research Intern
- Research in computer vision under the guidance of my host Manoj Kumar and co-host Neil Houlsby.
- Implemented feature caching and corresponding training pipelines for several pretrained Vision Transformer models.
- Investigated transfer learning capabilities and the effect of data augmentation methods in the feature space.
- Contributed to Scenic core.
- The results are summarized in a paper which was presented at CVPR 2024!
Software Quality Assurance Engineer
- Worked in a team of (software) engineers with 10+ years of software development experience.
- Assisted software quality assessments of OEM suppliers according to ISO 26262.
Projects
Publications
(2024).
Foundation Models for Amodal Video Instance Segmentation in Automated Driving.
In Proc. of ECCV - Workshops.
(2024).
Non-Causal to Causal SSL-Supported Transfer Learning: Towards A High-Performance Low-Latency Speech Vocoder.
In Proc. of IWAENC.
(2024).
Frozen Feature Augmentation for Few-Shot Image Classification.
In Proc. of CVPR.
(2023).
A Novel Benchmark for Refinement of Noisy Localization Labels in Autolabeled Datasets for Object Detection.
In Proc. of CVPR - Workshops.
(2023).
Improvements to Image Reconstruction-Based Performance Prediction for Semantic Segmentation in Highly Automated Driving.
In Proc. of CVPR - Workshops.
(2022).
Detecting Adversarial Perturbations in Multi-Task Perception.
In Proc. of IROS.
(2022).
Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression.
In Proc. of CVPR - Workshops.
(2022).
Performance Prediction for Semantic Segmentation by a Self-Supervised Image Reconstruction Decoder.
In Proc. of CVPR - Workshops.
(2022).
Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation.
In Deep Neural Networks and Data for Automated Driving.
(2022).
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety.
In Deep Neural Networks and Data for Automated Driving.