UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web

Author:

Yan Yibo1ORCID,Wen Haomin2ORCID,Zhong Siru1ORCID,Chen Wei1ORCID,Chen Haodong3ORCID,Wen Qingsong4ORCID,Zimmermann Roger5ORCID,Liang Yuxuan1ORCID

Affiliation:

1. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

2. Beijing Jiaotong University, Beijing, China

3. Northwestern Polytechnical University Xi'an, Xi'an, China

4. DAMO Academy, Alibaba Group, Seattle, USA

5. National University of Singapore, Singapore, Singapore

Publisher

ACM

Reference103 articles.

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2. Rohan Anil Andrew M Dai Orhan Firat Melvin Johnson Dmitry Lepikhin Alexandre Passos Siamak Shakeri Emanuel Taropa Paige Bailey Zhifeng Chen et al. 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403 (2023).

3. Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, et al. 2023. Openflamingo: An open-source framework for training large autoregressive vision-language models. arXiv preprint arXiv:2308.01390 (2023).

4. Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, and Stefano Ermon. 2020. Generating interpretable poverty maps using object detection in satellite images. arXiv preprint arXiv:2002.01612 (2020).

5. Efficient Poverty Mapping from High Resolution Remote Sensing Images

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1. ReFound: Crafting a Foundation Model for Urban Region Understanding upon Language and Visual Foundations;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

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