Few-Shot for quality inspection: what IADGPT changes when you only have a few images

Industrial production line: automated quality inspection with IADGPT and few images - ARCY Vision 2025

In brief: In industry, collecting hundreds of "defective" examples per reference is costly and often impossible at product launch. Few-shot approaches with vision-language models (LVLM) like IADGPT revolutionize quality inspection with only a few reference images.

1) Technology & challenges

In industry, collecting hundreds of "defective" examples per reference is costly and often impossible at product launch. Few-shot approaches with vision-language models (LVLM) fill this gap: the IADGPT pre-publication shows that a single model can detect, localize and explain anomalies from a few reference images, thanks to progressive training and in-context learning.

In parallel, the new MVTec AD 2 dataset raises the bar (transparency, backlight/dark-field, micro-defects) and reminds us that robustness in real conditions is the real judge (SOTA performance < 60% AU-PRO on average).

2) Problem & solution approach

Typical problem: starting visual quality control on a new SKU with few images.

Pragmatic approach: build 10–20 "good" images (+ some defects if available), evaluate a classic baseline and a few-shot LVLM approach like IADGPT on readable metrics (AUROC image-level, AU-PRO pixel-level) and stress the system under lighting/pose variations inspired by AD 2.

This protocol, backed by historical MVTec AD benchmarks, allows to quickly obtain a "go/no-go" signal and objectify gains before pilot deployment.

Bottles on production line: anomaly detection with few-shot approaches and LVLM models

3) Conclusion & ARCY

At ARCY, we integrate these few-shot and LVLM approaches into our quality inspection and industrial vision solutions (defects, counting, PPE), with end-to-end support: KPI scoping, supervised POC, on-prem/edge deployment if needed.

Are you studying a startup with little data but strong traceability requirements? Contact us to design a short, measurable and transferable protocol for production.

4) Sources (selection)

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