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

Research question and approach

Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still formulate diagnosis as pixel-field regression, making it difficult to localize variable-cardinality defects and bind semantic reasons to individual failures.

When this work is relevant

Use this work for structured feedback about where a defect is, what it is, why it matters and its importance; consult the source for the grounding and feedback protocol.

Author-written abstract

Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality. While recent dense-feedback methods move beyond scalar supervision, their heatmap-centric representations still formulate diagnosis as pixel-field regression, making it difficult to localize variable-cardinality defects and bind semantic reasons to individual failures. To address this representation bottleneck, we propose Structured Defect Grounding (SDG), which casts T2I diagnosis as structured set prediction by modeling each defect as a (location, type, reason, importance) tuple. To make this formulation trainable and measurable, we introduce SDG-30K, a 30K-image dataset with box-grounded annotations across four modern T2I generators, together with a dedicated evaluation protocol, SDG-Eval. Building on this structured representation, we further present a diagnosis-to-alignment framework in which a Vision-Language Model (VLM) serves as the SDG detector, and BoxFlow-GRPO converts predicted defect sets into box-derived, importance-weighted spatial rewards for diffusion model alignment. Extensive experiments show that our SDG detector outperforms leading proprietary VLMs on structured defect grounding, while SDG-guided rewards consistently improve T2I alignment and support localized image refinement. These results establish SDG as a unified, instance-level interface for diagnosing, evaluating, and enhancing modern generative models.

Abstract source: https://arxiv.org/abs/2606.06113. Checked 2026-09-14. Bibliographic metadata uses the linked paper record or author-maintained catalog. Results, limitations and experimental settings remain defined by the original source.

Citation

BibTeX · CITATION.cff

BibTeX is preserved from the author-maintained citation repository. The CFF uses the source metadata shown on this page. Version titles or author lists can differ; choose the version you used.

@article{zhang2026and,
  title={Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback},
  author={Zhang, Huaisong and Yu, Hao and Zhang, Yuxuan and Wang, Jiahe and Chen, Xinrui and Cao, Haoxiang and Lu, Feng and Zhang, Wendong and Yu, Changqian and Yuan, Chun},
  journal={arXiv preprint arXiv:2606.06113},
  year={2026}
}