I’m a researcher of artificial intelligence at DFKI and RPTU Kaiserslautern-Landau. My research interests include efficient deep learning, transformer models, multimodal learning, and computer vision. In my PhD project, my focus lies on the development of efficient transformer models for vision, language, and multimodal tasks.
CutMix assigns labels by patch area, not by visible object content, a systematic bias that mislabels 21.5% of samples and creates ghost labels in 17%. OA-CutMix replaces the label with one derived from object area, leaving the image mixing unchanged. It matches or beats 10+ static and dynamic mixing methods across 4 architectures and 6 datasets.
We use a frozen text encoder on image captions as a lightweight training-time auxiliary objective for image classifiers. The text components are dropped at inference, leaving a fast, unimodal vision model. Accuracy on ImageNet improves by up to +2.7 p.p. and downstream transfer by +1.0 p.p. on average, outperforming vision knowledge distillation at a fraction of the compute.
We improve the training of vision transformers by segmenting and recombining objects and backgrounds from datasets. This makes the transformers more accurate, as well as more robust.
An open-source tracker of AI and machine learning conference deadlines, featuring advanced filtering by rating, h5-index, and timeline. Designed to help researchers plan submissions efficiently across 90+ venues.
Energy- and data-saving methods for environmental perception in embedded AI systems using the case study of smart factory and smart farming applications; funded by the Carl Zeiss Foundation.
At its core, Albatross is a research project in the area of continual learning.
SustainML is dedicated to creating a sustainable ML framework for Green AI. By prioritizing energy efficiency, SustainML aims to pave the way for environmentally conscious AI solutions that are both efficient and effective.
If you have any questions, want to collaborate, or just want to chat, feel free to reach out.