Active few-shot segmentation by reinforcing data selection
arXiv cs.CV5d4 min read
arXiv:2607.22371v1 Announce Type: new Abstract: Few-shot learning enables medical image segmentation models to adapt to new tasks using only a small number of labelled examples. However, adaptation performance depends strongly on which examples are selected for the support set. Effective support sets should capture relevant variation within the target domain and be informative for adaptation, with constituent samples providing complementary information. Despite this, existing active data selection approaches largely prioritise samples individually and do not explicitly account for interactions
