Efficiently searching for relevant case studies is critical in architectural design, as designers rely on precedent examples to guide or inspire their ongoing projects. However, traditional text-based search tools struggle to capture the inherently visual and complex nature of architectural knowledge, often leading to time-consuming and imprecise exploration. This paper introduces ArchSeek, an innovative case study search system with recommendation capability, tailored for architecture design professionals. Powered by the visual understanding capabilities from vision-language models and cross-modal embeddings, it enables text and image queries with fine-grained control, and interaction-based design case recommendations. It offers architects a more efficient, personalized way to discover design inspirations, with potential applications across other visually driven design fields. The source code is available at https://github.com/danruili/ArchSeek.
@article{arxiv.2503.18680,
title = {ArchSeek: Retrieving Architectural Case Studies Using Vision-Language Models},
author = {Danrui Li and Yichao Shi and Yaluo Wang and Ziying Shi and Mubbasir Kapadia},
journal= {arXiv preprint arXiv:2503.18680},
year = {2025}
}