Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models
This addresses the challenge of creating effective ad banners by improving layout quality, though it appears incremental as it builds on existing VLM techniques for a specific domain.
The paper tackled the problem of generating layouts for image-based advertisements by proposing a method that uses a Vision-Language Model to analyze image content and produce placement plans, resulting in higher-quality layouts compared to existing methods.
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.