# ArchSeek: Retrieving Architectural Case Studies Using Vision-Language Models

arXiv: https://arxiv.org/abs/2503.18680 · HTML: https://mlsanity.com/paper/2503.18680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks](https://mlsanity.com/paper/2311.01361/) (2023)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [TechGPT-2.0: A large language model project to solve the task of knowledge graph construction](https://mlsanity.com/paper/2401.04507/) (2024)
- [Towards Multi-Language Recipe Personalisation and Recommendation](https://mlsanity.com/paper/2007.13440/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM](https://mlsanity.com/paper/2609.17181/) (2026)

## Abstract (arXiv)

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.