RADIOlogy: A Single-Pass Approach to Training-Free Zero-Shot Anomaly Detection

The paper introduces RADIOlogy, a single‑pass, training‑free method for zero‑shot anomaly detection that leverages the inherent multi‑head attention pooling of vision‑language models. Unlike prior approaches that depend on complex prompt‑learning or sliding‑window strategies, RADIOlogy uses a contrastive scoring scheme comparing vision features to normal and abnormal prompts, achieving superior performance without dense execution. The method also incorporates a dense anomaly refinement pipeline for pixel‑level segmentation and demonstrates applicability to medical imaging with minimal adjustments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

• llms
• computer-vision
• multimodal
• safety