AI RESEARCH
Scaling In-Context Segmentation with Hierarchical Supervision
arXiv CS.CV
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ArXi:2604.12752v1 Announce Type: new In-context learning (ICL) enables medical image segmentation models to adapt to new anatomical structures from limited examples, reducing the clinical annotation burden. However, standard ICL methods typically rely on dense, global cross-attention, which scales poorly with image resolution. While recent approaches have