1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning
Tidiane Camaret Ndir, André Biedenkapp, Noor Awad
In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional t…