13 papers · 1 filter
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
Alexander Jaus, Zdravko Marinov, Constantin Seibold +4
Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models,…
Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks
Pradnya Halady, Jiale Wei, Zdravko Marinov +2
Visual in-context learning has been proposed as a pathway towards dynamic models that can generate predictions based on a provided context and thereby can adapt to new vision tasks…
Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model
Sunil Khatri, Steven Landgraf, Markus Ulrich +1
Visual in-Context Learning (VICL) aims at making progress towards adaptive vision models, that can -- based on a few examples -- adapt to a new task at test-time. With the history…
From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks
Carlos Schmidt, Simon ReiÃ
Visual in-context learning models are designed to adapt to new tasks by leveraging a set of example input-output pairs, enabling rapid generalization without task-specific fine-tun…
Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective
Jonas Muth, Zdravko Marinov, Simon ReiÃ
While much of the medical computer vision community has focused on advancing performance for specific tasks, the underlying relationships between tasks, i.e., how they relate, over…
Is Visual in-Context Learning for Compositional Medical Tasks within Reach?
Simon ReiÃ, 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.…