ArchSeek: Retrieving Architectural Case Studies Using Vision-Language Models

Danrui Li1*, Yichao Shi2, Yaluo Wang3, Ziying Shi4, Mubbasir Kapadia1, 5
1Rutgers University 2Georgia Institute of Technology 3Havard University 4Southeast University 5Roblox CAAD Futures 2025
Paper Code arXiv

Our work augments the original design case materials by Vision Language Models, then establishes a fusion search pipeline with cross-modal capabilities, facilitating three user interaction modes.

Abstract

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.

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BibTeX

@misc{li2025archseek,
      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},
      year={2025},
      eprint={2503.18680},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2503.18680}, 
    }