IAD-Unify: Task-Specific Interfaces for Industrial Anomaly Understanding, Segmentation, and Generation
Read the original on arXiv AI →IAD-Unify is a unified architecture that connects a multimodal language model, a dense visual expert, and a diffusion editor via task‑specific token interfaces to perform industrial anomaly inspection tasks such as explaining defects, localizing pixels, and generating controlled edits. It uses a multi‑reference DINOv2 pathway to create a dense anomaly field, compresses it into structured evidence tokens, and feeds these tokens to Qwen3.5 for grounded answers and semantic residuals, while a separate query interface integrates with Stable Diffusion for localized generation. The system is trained in stages—first aligning dense evidence with language, then calibrating segmentation, and finally pre‑training the diffusion editor—while also building a large, quality‑controlled dataset of anomaly evidence and edit pairs.
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