A zero-shot anomaly segmentation pipeline that generates image-specific defect prompts using an image tagger and an LLM improves F1-max by up to 10% over fixed prompts.
The Visual Computer 36(1), 85–96 (2020), https://github.com/abin24/ Magnetic-tile-defect-datasets./
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
A zero-shot anomaly segmentation pipeline that generates image-specific defect prompts using an image tagger and an LLM improves F1-max by up to 10% over fixed prompts.