💬 LLM (Other)¶

📷 CVPR2026 · 2 paper notes

🔥 Top topics: Layout & Composition ×2 · LLM ×2

LLM-Guided Probabilistic Fusion for Label-Efficient Document Layout Analysis: This paper integrates text-pretrained LLMs as "structural prior generators" into the pseudo-label refinement stage of semi-supervised layout detection. By using OCR+LLM to infer document hierarchical regions and performing inverse variance probabilistic fusion (including learnable instance-adaptive gating) with teacher detector outputs, the method achieves 88.2 AP (lightweight backbone) and 89.7 AP (LayoutLMv3) on PubLayNet using only 5% labels, with the most significant gains observed in rare layout elements such as titles and headers.
OmniDocLayout: Towards Diverse Document Layout Generation via Coarse-to-Fine LLM Learning: Addressing the limitation that existing document layout generation data are "academic-only with single styles," the authors first create OmniDocLayout-1M, the first million-scale diverse layout dataset covering six document categories. They then employ a 0.5B small LLM using a "coarse-to-fine" paradigm—learning general layout rules on multi-domain coarse labels followed by adapting to specific domains with few fine labels. This approach outperforms both specialized layout models and general large models such as GPT-4o/Gemini/Claude on M6Doc.