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

TL;DR

This paper introduces a novel two-stage method using vision-language models to generate more effective and content-aware advertisement layouts by analyzing image content and producing detailed placement plans.

Contribution

The paper presents a new two-stage pipeline leveraging vision-language models for content-aware ad banner layout generation, improving over traditional saliency-based methods.

Findings

Outperforms existing layout methods in quality metrics

Produces more semantically coherent advertisement layouts

Effectively integrates image content analysis into layout design

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.