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In few-shot industrial anomaly detection, the few normal target images provide no direct defect supervision, making anomaly prompts difficult to learn from these samples alone. Some vision-language methods therefore use manually specified descriptions to supply explicit anomaly semantics. However, constructing these de...
The results show that intervention-based diagnostics help identify stable-but-wrong and prior-tracking predictions that repeated agreement alone may miss, and a post-hoc, model-agnostic framework for accepting or rejecting individual VLM predictions is proposed, called Answer-Level Trust Selection (ATS).
This paper proposes IterCAD, an iterative framework that reformulates orthographic-view-to-CAD generation as a progressive program repair process that consistently improves code executability and geometric fidelity over strong one-shot baselines.
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