Diffusion-based Document Layout Generation
원문 초록
We develop a diffusion-based approach for various document layout sequence generation. Layout sequences specify the contents of a document design in an explicit format. Our novel diffusion-based approach works in the sequence domain rather than the image domain in order to permit more complex and realistic layouts. We also introduce a new metric, Document Earth Mover's Distance (Doc-EMD). By considering similarity between heterogeneous categories document designs, we handle the shortcomings of prior document metrics that only evaluate the same category of layouts. Our empirical analysis shows that our diffusion-based approach is comparable to or outperforming other previous methods for layout generation across various document datasets. Moreover, our metric is capable of differentiating documents better than previous metrics for specific cases.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Relation-Rich Visual Document Generator for Visual Information Extraction
MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse
FoREST: Frame of Reference Evaluation in Spatial Reasoning Tasks
Smaller But Better: Unifying Layout Generation with Smaller Large Language Models
DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout Generation
HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.