ArchSeek: Retrieving Architectural Case Studies Using Vision-Language Models
This addresses the challenge for architectural design professionals who struggle with time-consuming and imprecise searches due to the visual complexity of architectural knowledge, representing an incremental improvement over traditional text-based tools.
The paper tackles the problem of inefficient search for architectural case studies by introducing ArchSeek, a system that uses vision-language models to enable text and image queries with fine-grained control, offering architects a more efficient and personalized way to discover design inspirations.
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.