Summary of Zero-shot Building Age Classification From Facade Image Using Gpt-4, by Zichao Zeng et al.
Zero-shot Building Age Classification from Facade Image Using GPT-4
by Zichao Zeng, June Moh Goo, Xinglei Wang, Bin Chi, Meihui Wang, Jan Boehm
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a zero-shot building age classifier that estimates the age of construction using pre-trained vision language models (VLMs) like GPT-4 Vision. The approach utilizes prompts with logical instructions to classify facade images without requiring labelled training data. The study introduces a new dataset, FI-London, comprising facade images and building age epochs, and evaluates the model’s performance on this dataset. Although the classifier achieves a modest accuracy of 39.69%, it shows promise in predicting building age epochs successfully, albeit with a small bias. However, the model struggles to predict the age of very old buildings and fine-grained predictions within 2 decades. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses pre-trained VLMs like GPT-4 Vision to estimate building ages from facade images without needing labelled training data. The approach is promising for predicting building age epochs, but it struggles with very old buildings and fine-grained predictions. The study creates a new dataset called FI-London, which contains facade images and building age epochs. |
Keywords
» Artificial intelligence » Gpt » Zero shot
