# LayoutGMN: Neural Graph Matching for Structural Layout Similarity

arXiv: https://arxiv.org/abs/2012.06547 · HTML: https://mlsanity.com/paper/2012.06547/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 19 of 19)

- [Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation](https://mlsanity.com/paper/2104.13316/) (2021)
- [Optimal Correction Cost for Object Detection Evaluation](https://mlsanity.com/paper/2203.14438/) (2022)
- [LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation](https://mlsanity.com/paper/2407.12356/) (2024)
- [Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning](https://mlsanity.com/paper/2303.00977/) (2023)
- [DocSynthv2: A Practical Autoregressive Modeling for Document Generation](https://mlsanity.com/paper/2406.08354/) (2024)
- [Self-supervised Photographic Image Layout Representation Learning](https://mlsanity.com/paper/2403.03740/) (2024)
- [Representation Learning for Sequential Volumetric Design Tasks](https://mlsanity.com/paper/2309.02583/) (2023)
- [SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity](https://mlsanity.com/paper/2309.04357/) (2023)
- [LayoutGKN: Graph Similarity Learning of Floor Plans](https://mlsanity.com/paper/2509.03737/) (2025)
- [Cross-Domain Document Layout Analysis Using Document Style Guide](https://mlsanity.com/paper/2201.09407/) (2022)
- [Neural Graph Matching for Modification Similarity Applied to Electronic Document Comparison](https://mlsanity.com/paper/2204.05486/) (2022)
- [Generating Topological Structure of Floorplans from Room Attributes](https://mlsanity.com/paper/2204.12338/) (2022)
- [AutoGameUI: Constructing High-Fidelity GameUI via Multimodal Correspondence Matching](https://mlsanity.com/paper/2411.03709/) (2024)
- [Normalized Matching Transformer](https://mlsanity.com/paper/2503.17715/) (2025)
- [Learning Aesthetic Layouts via Visual Guidance](https://mlsanity.com/paper/2107.06262/) (2021)
- [Uncovering the Origins of Instability in Dynamical Systems: How Attention Mechanism Can Help?](https://mlsanity.com/paper/2212.09641/) (2022)
- [Aggregated Structural Representation with Large Language Models for Human-Centric Layout Generation](https://mlsanity.com/paper/2505.19554/) (2025)
- [UISearch: Graph-Based Embeddings for Multimodal Enterprise UI Screenshots Retrieval](https://mlsanity.com/paper/2511.19380/) (2025)
- [SPRITE: From Static Mockups to Engine-Ready Game UI](https://mlsanity.com/paper/2604.18591/) (2026)

## Abstract (arXiv)

We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements.