Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning

The Similarity-as-Evidence (SaE) framework addresses overconfidence in Vision-Language Models used for medical active learning by calibrating text-image similarities to better select informative samples for annotation. This approach improves accuracy and calibration in medical imaging datasets, offering content creators a more efficient and interpretable method for active learning with limited labeled data.

Read the full article at arXiv cs.CV (Vision)

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.