Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.18680 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910890281926656 |
|---|---|
| author | Li, Danrui Shi, Yichao Wang, Yaluo Shi, Ziying Kapadia, Mubbasir |
| author_facet | Li, Danrui Shi, Yichao Wang, Yaluo Shi, Ziying Kapadia, Mubbasir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18680 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ArchSeek: Retrieving Architectural Case Studies Using Vision-Language Models Li, Danrui Shi, Yichao Wang, Yaluo Shi, Ziying Kapadia, Mubbasir Information Retrieval Computation and Language 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. |
| title | ArchSeek: Retrieving Architectural Case Studies Using Vision-Language Models |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2503.18680 |