3 citations · 8 across the 8 of their papers we have counts for
20 papers
Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology
Yusuf Salcan, Simon Ging, Robin Tibor Schirrmeister +4
We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a large-scale bilingual (German/Engli…
Scaling In-Context Segmentation with Hierarchical Supervision
T. Camaret Ndir, Marco Reisert, Robin T. Schirrmeister
In-context learning (ICL) enables medical image segmentation models to adapt to new anatomical structures from limited examples, reducing the clinical annotation burden. However, s…
Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training
Tidiane Camaret Ndir, Alexander Pfefferle, Robin Tibor Schirrmeister
Interactive 3D biomedical image segmentation requires efficient models that can iteratively refine predictions based on user prompts. Current foundation models either lack volumetr…
EEG-CLIP : Learning EEG representations from natural language descriptions
Tidiane Camaret Ndir, Robin Tibor Schirrmeister, Tonio Ball
Deep networks for electroencephalogram (EEG) decoding are often only trained to solve one specific task, such as pathology or age decoding. A more general task-agnostic approach is…
Unlocking In-Context Learning for Natural Datasets Beyond Language Modelling
Jelena Bratulić, Sudhanshu Mittal, David T. Hoffmann +5
Large Language Models (LLMs) exhibit In-Context Learning (ICL), which enables the model to perform new tasks conditioning only on the examples provided in the context without updat…
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova +5
While tabular classification has traditionally relied on from-scratch training, a recent breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to…