Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to discover images for similarity-based search that can adequately express their intent. We propose GenQuery, a novel system that integrates generative models into the visual search process. GenQuery can automatically elaborate on users' queries and surface concrete search directions when users only have abstract ideas. To support precise expression of search intents, the system enables users to generatively modify images and use these in similarity-based search. In a comparative user study (N=16), designers felt that they could more accurately express their intents and find more satisfactory outcomes with GenQuery compared to a tool without generative features. Furthermore, the unpredictability of generations allowed participants to uncover more diverse outcomes. By supporting both convergence and divergence, GenQuery led to a more creative experience.

翻译：设计师在早期设计阶段依赖视觉搜索来探索和发展想法。然而，设计师难以确定合适的文本查询以启动搜索，或难以发现用于基于相似性搜索的图像来充分表达其意图。我们提出GenQuery，一种将生成模型集成到视觉搜索过程中的新型系统。当用户仅持有抽象概念时，GenQuery能自动细化用户查询并呈现具体的搜索方向。为支持搜索意图的精准表达，该系统允许用户生成式修改图像，并将其用于基于相似性的搜索。在一项对比用户研究（N=16）中，设计师认为与未集成生成功能的工具相比，使用GenQuery能更准确地表达意图并找到更满意的结果。此外，生成结果的不确定性使参与者能够发现更多样化的成果。通过同时支持收敛与发散，GenQuery带来了更具创造性的体验。