While infographics have become a powerful medium for communicating data-driven stories, authoring them from scratch remains challenging, especially for novice users. Retrieving relevant exemplars from a large corpus can provide design inspiration and promote reuse, substantially lowering the barrier to infographic authoring. However, effective retrieval is difficult because users often express design intent in ambiguous natural language, while infographics embody rich and multi-faceted visual designs. As a result, keyword-based search often fails to capture design intent, and general-purpose vision-language retrieval models trained on natural images are ill-suited to the text-heavy, multi-component nature of infographics. To address these challenges, we develop an intent-aware infographic retrieval framework that better aligns user queries with infographic designs. We first conduct a formative study of how people describe infographics and derive an intent taxonomy spanning content and visual design facets. This taxonomy is then leveraged to enrich and refine free-form user queries, guiding the retrieval process with intent-specific cues. Building on the retrieved exemplars, users can adapt the designs to their own data with high-level edit intents, supported by an interactive agent that performs low-level adaptation. Both quantitative evaluations and user studies are conducted to demonstrate that our method improves retrieval quality over baseline methods while better supporting intent satisfaction and efficient infographic authoring.

翻译：尽管信息图已成为传达数据驱动故事的有力媒介，但从头创作信息图仍具挑战性，尤其是对于新手用户而言。从大型语料库中检索相关范例可提供设计灵感并促进复用，从而大幅降低信息图创作的门槛。然而，有效检索面临困难，因为用户常以模糊的自然语言表达设计意图，而信息图则蕴含丰富且多层面的视觉设计。因此，基于关键词的搜索往往难以捕捉设计意图，且基于自然图像训练的通用视觉-语言检索模型也不适用于信息图这种文本密集、多组件的特性。为解决这些挑战，我们开发了一个意图感知的信息图检索框架，能够更好地对齐用户查询与信息图设计。我们首先开展了一项形成性研究，了解人们如何描述信息图，并推导出一个涵盖内容和视觉设计层面的意图分类体系。随后，利用这一分类体系来丰富和完善自由形式的用户查询，通过意图特定的线索引导检索过程。基于检索到的范例，用户可通过高级编辑意图将设计适配到自身数据，并由一个执行低级适配的交互式代理提供支持。通过定量评估和用户研究，我们证明该方法在提升检索质量方面优于基线方法，同时能更好地满足用户意图并支持高效的信息图创作。