6 citations · 10 across the 10 of their papers we have counts for
4 papers · 1 filter
Is Visual in-Context Learning for Compositional Medical Tasks within Reach?
Simon Reiß, Zdravko Marinov, Alexander Jaus +4
In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks and adapt to new tasks during test time without re-training.…
On the Domain Robustness of Contrastive Vision-Language Models
Mario Koddenbrock, Rudolf Hoffmann, David Brodmann +1
In real-world vision-language applications, practitioners increasingly rely on large, pretrained foundation models rather than custom-built solutions, despite limited transparency…
Feedback-driven object detection and iterative model improvement
Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner
Automated object detection has become increasingly valuable across diverse applications, yet efficient, high-quality annotation remains a persistent challenge. In this paper, we pr…
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification
Dennis Ritter, Mike Hemberger, Marc Hönig +3
In this paper, we systematically analyze unsupervised domain adaptation pipelines for object classification in a challenging industrial setting. In contrast to standard natural obj…