6 citations · 10 across the 10 of their papers we have counts for
3 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…
Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation
Justus Westerhoff, Golzar Atefi, Mario Koddenbrock +4
The capacity of foundation models allows for their application to new, unseen tasks. The adaptation to such tasks is called transfer learning. An efficient transfer learning method…