AnomalyGPT
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Applies large vision-language models to industrial anomaly detection with synthetic data augmentation.
Synthetic anomaly data: Simulates anomalous images and textual descriptions to generate training data, addressing the scarcity of real anomaly examples in industrial settings.
Image decoder + prompt learner: Combines an image decoder with a prompt learner to detect and localize anomalies in product images.
Few-shot ICL: Demonstrates few-shot in-context learning capabilities, adapting to new product types from a handful of examples.
SoTA on industrial benchmarks: Achieves state-of-the-art performance on standard industrial anomaly-detection benchmarks, validating the VLM approach for manufacturing QA.
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