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

• URL: http://arxiv.org/abs/2512.12596v1
• Date: Sun, 14 Dec 2025 08:30:15 GMT
• Title: Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models
• Authors: Kei Yoshitake, Kento Hosono, Ken Kobayashi, Kazuhide Nakata,
• Abstract summary: We propose a method for generating layouts for image-based advertisements by leveraging a Vision-Language Model (VLM)<n>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.
• Score: 3.0133884087546536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• UM-Text: A Unified Multimodal Model for Image Understanding [11.870303482927541]
  We propose a unified multimodal model for context understanding and visual text editing by natural language instructions.<n>We introduce a Visual Language Model (VLM) to process the instruction and reference image.<n>We propose a regional consistency loss to offer more effective supervision for glyph generation on both latent and RGB space.
  arXiv  Detail & Related papers  (2026-01-13T08:18:49Z)
• Generating Intermediate Representations for Compositional Text-To-Image Generation [16.757550214291015]
  We propose a compositional approach for text-to-image generation based on two stages. In the first stage, we design a diffusion-based generative model to produce one or more aligned intermediate representations conditioned on text. In the second stage, we map these representations, together with the text, to the final output image using a separate diffusion-based generative model.
  arXiv  Detail & Related papers  (2024-10-13T10:24:55Z)
• A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models [117.77807994397784]
  Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs.
  arXiv  Detail & Related papers  (2024-06-20T17:58:52Z)
• Tuning-Free Image Customization with Image and Text Guidance [65.9504243633169]
  We introduce a tuning-free framework for simultaneous text-image-guided image customization. Our approach preserves the semantic features of the reference image subject while allowing modification of detailed attributes based on text descriptions. Our approach outperforms previous methods in both human and quantitative evaluations.
  arXiv  Detail & Related papers  (2024-03-19T11:48:35Z)
• Prompting Large Vision-Language Models for Compositional Reasoning [12.908633583017359]
  We develop a novel generative method that prompts large vision-language models to depict images and perform compositional reasoning. Our method outperforms other embedding-based methods on the Winoground dataset, and obtains further improvement of up to 10% accuracy when enhanced with the optimal description.
  arXiv  Detail & Related papers  (2024-01-20T22:04:28Z)
• Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis [15.76266032768078]
  This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries.<n>We first introduce vision guidance as a foundational spatial cue within the perturbed distribution.<n>We propose a universal framework, Layered Rendering Diffusion (LRDiff), which constructs an image-rendering process with multiple layers.
  arXiv  Detail & Related papers  (2023-11-30T10:36:19Z)
• RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment [112.45442468794658]
  We propose a two-stage coarse-to-fine semantic re-alignment method, named RealignDiff. In the coarse semantic re-alignment phase, a novel caption reward is proposed to evaluate the semantic discrepancy between the generated image caption and the given text prompt. The fine semantic re-alignment stage employs a local dense caption generation module and a re-weighting attention modulation module to refine the previously generated images from a local semantic view.
  arXiv  Detail & Related papers  (2023-05-31T06:59:21Z)
• Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning [6.470760375991825]
  We propose a domain-agnostic i2i method for fine-grained problems, where the domains are related. We present a novel approach that relies on training the generative model to produce images that both share distinctive information of the associated source image. We validate our method on the CelebA-HQ and AFHQ datasets by demonstrating improvements in terms of visual quality.
  arXiv  Detail & Related papers  (2023-05-08T19:58:49Z)
• SpaText: Spatio-Textual Representation for Controllable Image Generation [61.89548017729586]
  SpaText is a new method for text-to-image generation using open-vocabulary scene control. In addition to a global text prompt that describes the entire scene, the user provides a segmentation map. We show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-conditional-based.
  arXiv  Detail & Related papers  (2022-11-25T18:59:10Z)
• Towards Open-World Text-Guided Face Image Generation and Manipulation [52.83401421019309]
  We propose a unified framework for both face image generation and manipulation. Our method supports open-world scenarios, including both image and text, without any re-training, fine-tuning, or post-processing.
  arXiv  Detail & Related papers  (2021-04-18T16:56:07Z)
• TediGAN: Text-Guided Diverse Face Image Generation and Manipulation [52.83401421019309]
  TediGAN is a framework for multi-modal image generation and manipulation with textual descriptions. StyleGAN inversion module maps real images to the latent space of a well-trained StyleGAN. visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. instance-level optimization is for identity preservation in manipulation.
  arXiv  Detail & Related papers  (2020-12-06T16:20:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.