arXiv Machine Learning
Detect Anything in Graphic Design: Element-Level Rewards for Autoregressive Detection
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Beyond Atomic Layouts: Compositional Design Understanding with Vision-Language Models
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Hugging Face Trending Papers
Aug 27
Beyond Atomic Layouts: Compositional Design Understanding with Vision-Language Models
The paper introduces a new task called compositional layout understanding, focusing on interpreting complex, multi-layer document and UI layouts that involve hierarchical relationships among visually entangled elements. It presents CoDeLayout, a VQA dataset of about 20,000 real-world layouts annotated with compositional element pairs and design intent, and identifies two main challenges for current vision‑language models: semantic drift between textual metadata and visual content, and structural ambiguity in hierarchical inter‑element relationships. To address these, the authors propose MASON, a post‑training paradigm that combines multimodal alignment and structural perception, achieving a 91.66% accuracy on CoDeLayout and outperforming full‑data direct fine‑tuning with only 30% of the training data.