# Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation

arXiv: https://arxiv.org/abs/2311.13602 · HTML: https://mlsanity.com/paper/2311.13602/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 39 of its 55 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Placed in

- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data](https://mlsanity.com/paper/2306.09344/) (2023)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [KNN-Diffusion: Image Generation via Large-Scale Retrieval](https://mlsanity.com/paper/2204.02849/) (2022)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Highly Accurate Dichotomous Image Segmentation](https://mlsanity.com/paper/2203.03041/) (2022)
- [Variational Transformer Networks for Layout Generation](https://mlsanity.com/paper/2104.02416/) (2021)
- [Neural Design Network: Graphic Layout Generation with Constraints](https://mlsanity.com/paper/1912.09421/) (2019)
- [Constrained Graphic Layout Generation via Latent Optimization](https://mlsanity.com/paper/2108.00871/) (2021)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout](https://mlsanity.com/paper/2303.15937/) (2023)
- [Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs](https://mlsanity.com/paper/2205.00303/) (2022)

## Built on by (most cited first, 30 of 35)

- [CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation](https://mlsanity.com/paper/2412.03859/) (2024)
- [PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generation](https://mlsanity.com/paper/2404.00995/) (2024)
- [Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook](https://mlsanity.com/paper/2503.18016/) (2025)
- [CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation](https://mlsanity.com/paper/2506.10890/) (2025)
- [Multimodal Markup Document Models for Graphic Design Completion](https://mlsanity.com/paper/2409.19051/) (2024)
- [PosterO: Structuring Layout Trees to Enable Language Models in Generalized Content-Aware Layout Generation](https://mlsanity.com/paper/2505.07843/) (2025)
- [VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correction](https://mlsanity.com/paper/2412.04237/) (2024)
- [Uni-Layout: Integrating Human Feedback in Unified Layout Generation and Evaluation](https://mlsanity.com/paper/2508.02374/) (2025)
- [From Elements to Design: A Layered Approach for Automatic Graphic Design Composition](https://mlsanity.com/paper/2412.19712/) (2024)
- [R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generation](https://mlsanity.com/paper/2408.09743/) (2024)
- [Rethinking Layered Graphic Design Generation with a Top-Down Approach](https://mlsanity.com/paper/2507.05601/) (2025)
- [PosterVerse: A Full-Workflow Framework for Commercial-Grade Poster Generation with HTML-Based Scalable Typography](https://mlsanity.com/paper/2601.03993/) (2026)
- [OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities](https://mlsanity.com/paper/2507.15085/) (2025)
- [MultiBanana: A Challenging Benchmark for Multi-Reference Text-to-Image Generation](https://mlsanity.com/paper/2511.22989/) (2025)
- [ProCrop: Learning Aesthetic Image Cropping from Professional Compositions](https://mlsanity.com/paper/2505.22490/) (2025)
- [LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation](https://mlsanity.com/paper/2504.10829/) (2025)
- [CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design](https://mlsanity.com/paper/2506.21934/) (2025)
- [SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior](https://mlsanity.com/paper/2510.15749/) (2025)
- [Design-o-meter: Towards Evaluating and Refining Graphic Designs](https://mlsanity.com/paper/2411.14959/) (2024)
- [Smaller But Better: Unifying Layout Generation with Smaller Large Language Models](https://mlsanity.com/paper/2502.14005/) (2025)
- [SciPostGen: Bridging the Gap between Scientific Papers and Poster Layouts](https://mlsanity.com/paper/2511.22490/) (2025)
- [LayoutRAG: Retrieval-Augmented Model for Content-agnostic Conditional Layout Generation](https://mlsanity.com/paper/2506.02697/) (2025)
- [Scan-and-Print: Patch-level Data Summarization and Augmentation for Content-aware Layout Generation in Poster Design](https://mlsanity.com/paper/2505.20649/) (2025)
- [PSDesigner: Automated Graphic Design with a Human-Like Creative Workflow](https://mlsanity.com/paper/2603.25738/) (2026)
- [LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformer](https://mlsanity.com/paper/2407.15233/) (2024)
- [DesignLab: Designing Slides Through Iterative Detection and Correction](https://mlsanity.com/paper/2507.17202/) (2025)
- [LLMs as Layout Designers: Enhanced Spatial Reasoning for Content-Aware Layout Generation](https://mlsanity.com/paper/2509.16891/) (2025)
- [Automatic Text Box Placement for Supporting Typographic Design](https://mlsanity.com/paper/2510.07665/) (2025)
- [UniLayDiff: A Unified Diffusion Transformer for Content-Aware Layout Generation](https://mlsanity.com/paper/2512.08897/) (2025)
- [PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement](https://mlsanity.com/paper/2601.11747/) (2026)

## Abstract (arXiv)

Content-aware graphic layout generation aims to automatically arrange visual elements along with a given content, such as an e-commerce product image. In this paper, we argue that the current layout generation approaches suffer from the limited training data for the high-dimensional layout structure. We show that a simple retrieval augmentation can significantly improve the generation quality. Our model, which is named Retrieval-Augmented Layout Transformer (RALF), retrieves nearest neighbor layout examples based on an input image and feeds these results into an autoregressive generator. Our model can apply retrieval augmentation to various controllable generation tasks and yield high-quality layouts within a unified architecture. Our extensive experiments show that RALF successfully generates content-aware layouts in both constrained and unconstrained settings and significantly outperforms the baselines.