mlsanityData to 7 Oct 2026

Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models

arXiv 2512.12596 ↗First posted 14 Dec 2025cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Document understanding and multimodal RAG

Abstract (arXiv)

In this paper, we propose a method for generating layouts for image-based advertisements by leveraging a Vision-Language Model (VLM). Conventional advertisement layout techniques have predominantly relied on saliency mapping to detect salient regions within a background image, but such approaches often fail to fully account for the image's detailed composition and semantic content. To overcome this limitation, our method harnesses a VLM to recognize the products and other elements depicted in the background and to inform the placement of text and logos. The proposed layout-generation pipeline consists of two steps. In the first step, the VLM analyzes the image to identify object types and their spatial relationships, then produces a text-based "placement plan" based on this analysis. In the second step, that plan is rendered into the final layout by generating HTML-format code. We validated the effectiveness of our approach through evaluation experiments, conducting both quantitative and qualitative comparisons against existing methods. The results demonstrate that by explicitly considering the background image's content, our method produces noticeably higher-quality advertisement layouts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 20 of its 24 references that are in the map. First posted on arXiv 14 Dec 2025.

Builds on

20 of 24 references.

References · 20

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2020Denoising Diffusion Implicit Modelscited by 9,655
  7. 2023Visual Instruction Tuningcited by 7,792
  8. 2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 657
  9. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
  10. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 141
  11. 2020Attribute-conditioned Layout GAN for Automatic Graphic Designcited by 67
  12. 2023PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layoutcited by 60
8 more
  1. 2022Composition-aware Graphic Layout GAN for Visual-textual Presentation Designscited by 59
  2. 2023LayoutDM: Transformer-based Diffusion Model for Layout Generationcited by 57
  3. 2023LayoutPrompter: Awaken the Design Ability of Large Language Modelscited by 47
  4. 2024PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLMcited by 39
  5. 2024PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generationcited by 34
  6. 2023LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Modelscited by 17
  7. 2024Can GPTs Evaluate Graphic Design Based on Design Principles?cited by 14
  8. 2025BannerAgency: Advertising Banner Design with Multimodal LLM Agentscited by 11

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  1. 2025T-Stars-Poster: A Framework for Product-Centric Advertising Image Design
  2. 2024VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correction
  3. 2025Automatic Text Box Placement for Supporting Typographic Design
  4. 2025Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agents
  5. 2023Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation
  6. 2024LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformer
  7. 2025AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models
  8. 2023Planning and Rendering: Towards Product Poster Generation with Diffusion Models