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构思创意。然而，设计师常难以找到合适的文本查询来启动搜索，或难以发现可用于基于相似性搜索的、充分表达其意图的图像。我们提出GenQuery——一种将生成模型集成到视觉搜索流程中的新型系统。当用户仅有抽象想法时，GenQuery能自动细化用户的查询并呈现具体的搜索方向。为支持搜索意图的精确表达，该系统使用户能够生成式地修改图像，并将这些图像用于基于相似性的搜索。在对比用户研究（N=16）中，设计师认为与不含生成功能的工具相比，使用GenQuery能更精确地表达意图并找到更满意的结果。此外，生成结果的不可预测性使参与者能够发现更多样化的结果。通过同时支持收敛与发散，GenQuery带来了更具创造性的体验。