Summary of Musecl: Predicting Urban Socioeconomic Indicators Via Multi-semantic Contrastive Learning, by Xixian Yong et al.
MuseCL: Predicting Urban Socioeconomic Indicators via Multi-Semantic Contrastive Learning
by Xixian Yong, Xiao Zhou
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 A novel framework called Multi-Semantic Contrastive Learning (MuseCL) is proposed for fine-grained urban region profiling and socioeconomic prediction. The framework leverages multi-modal data, including street view images and remote sensing images, as well as text-based Point of Interest (POI) information. MuseCL constructs contrastive sample pairs to derive semantic features from visual and textual modalities, which are then merged using a cross-modality-based attentional fusion module. Experimental results demonstrate the superiority of MuseCL over baseline models, achieving an average improvement of 10% in R-squared. The code is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict socioeconomic indicators for cities has been developed. This method uses computer vision and natural language processing to analyze pictures and text from different parts of a city. The goal is to understand what makes certain areas more or less diverse, rich, or poor. By comparing these images and texts, the model can identify patterns that help it make accurate predictions about socioeconomic indicators like poverty rates or education levels. This approach has been tested in multiple cities and shows promising results. |
Keywords
* Artificial intelligence * Multi modal * Natural language processing
