mlsanityData to 14 Oct 2026

LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation

arXiv 2407.12356 ↗First posted 17 Jul 2024cs.CVcited by 10

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Abstract (arXiv)

We introduce a layout similarity measure designed to evaluate the results of layout generation. While several similarity measures have been proposed in prior research, there has been a lack of comprehensive discussion about their behaviors. Our research uncovers that the majority of these measures are unable to handle various layout differences, primarily due to their dependencies on strict element matching, that is one-by-one matching of elements within the same category. To overcome this limitation, we propose a new similarity measure based on optimal transport, which facilitates a more flexible matching of elements. This approach allows us to quantify the similarity between any two layouts even those sharing no element categories, making our measure highly applicable to a wide range of layout generation tasks. For tasks such as unconditional layout generation, where FID is commonly used, we also extend our measure to deal with collection-level similarities between groups of layouts. The empirical result suggests that our collection-level measure offers more reliable comparisons than existing ones like FID and Max.IoU.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 17 Jul 2024.

Builds on

28 of 41 references.

References · 28

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  10. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  11. 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 95
  12. 2021Variational Transformer Networks for Layout Generationcited by 88
16 more
  1. 2019Neural Design Network: Graphic Layout Generation with Constraintscited by 83
  2. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  3. 2021BLT: Bidirectional Layout Transformer for Controllable Layout Generationcited by 69
  4. 2023LayoutDM: Transformer-based Diffusion Model for Layout Generationcited by 57
  5. 2019READ: Recursive Autoencoders for Document Layout Generationcited by 51
  6. 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 48
  7. 2022LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restrictioncited by 43
  8. 2023Unifying Layout Generation with a Decoupled Diffusion Modelcited by 41
  9. 2023LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Modelscited by 41
  10. 2023PLay: Parametrically Conditioned Layout Generation using Latent Diffusioncited by 24
  11. 2023Diffusion-based Document Layout Generationcited by 21
  12. 2023DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformercited by 20
  13. 2020LayoutGMN: Neural Graph Matching for Structural Layout Similaritycited by 19
  14. 2022Optimal Correction Cost for Object Detection Evaluationcited by 11
  15. 2023A Survey for Graphic Design Intelligencecited by 9
  16. 2023Diverse Multimedia Layout Generation with Multi Choice Learningcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 3.

10 papers.