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arXiv Computer Vision By Huaisong Zhang, Hao Yu, Yuxuan Zhang, Jiahe Wang, Xinrui Chen, Haoxiang Cao, Feng Lu, Wendong Zhang, Changqian Yu, Chun Yuan

Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback

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The paper introduces Structured Defect Grounding (SDG), a method that treats text‑to‑image failure diagnosis as a structured set prediction problem, representing each defect as a tuple of location, type, reason, and importance. It presents SDG‑30K, a 30,000‑image dataset with box‑grounded annotations for four modern T2I generators, and a dedicated evaluation protocol, SDG‑Eval. Using a Vision‑Language Model as an SDG detector and a BoxFlow‑GRPO module, the authors demonstrate that SDG‑guided rewards improve diffusion model alignment and enable localized image refinement, positioning SDG as a unified interface for diagnosing and enhancing generative models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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