Graphic designers explore large stock image collections during open-ended or early-stage design tasks, yet common tools emphasize relevance and similarity, limiting designers' ability to overview the design space or discover visual patterns. We present an image exploration prototype that enables stepwise adjustment of diversity, allowing users to transition from diverse overviews to increasingly focused subsets during exploration. Our approach implements diversity control via determinantal point process (DPP)-based sampling and exposes diversity-similarity tradeoffs through interaction rather than static ranking. We report findings from a pilot study with professional graphic designers comparing our technique to baselines inspired by current tools in open-ended image selection tasks. Results suggest that stepwise diversity control supports early-stage sensemaking and comparison of visual patterns, while revealing important tradeoffs: diversity aids discovery and reduces backtracking, but becomes less desirable as exploration progresses. We aim to provide a novel perspective on how to implement transitions between diversity and similarity. Our code is available at https://github.com/CyberAgentAILab/DiverXplorer.

翻译：平面设计师在开放式或早期设计任务中需要探索大型素材图像库，然而现有工具通常强调相关性和相似性，这限制了设计师概览设计空间或发现视觉模式的能力。我们提出了一种图像探索原型，支持逐步调整多样性，允许用户在探索过程中从多样化的概览过渡到逐渐聚焦的图像子集。我们的方法通过基于行列式点过程（DPP）的采样实现多样性控制，并通过交互而非静态排序来呈现多样性与相似性的权衡关系。我们报告了与专业平面设计师进行的初步研究结果，该研究在开放式图像选择任务中将我们的技术与受现有工具启发的基线方法进行了比较。结果表明，逐步多样性控制有助于早期意义建构和视觉模式比较，同时也揭示了重要的权衡关系：多样性有助于发现并减少回溯操作，但随着探索进程推进会逐渐降低其必要性。我们旨在为如何实现多样性与相似性之间的过渡提供新的视角。我们的代码可在 https://github.com/CyberAgentAILab/DiverXplorer 获取。