{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatial-temporal-contrasting-for-fine-grained","title":"Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference","arxiv_id":null,"date":"2023-12-01","proceeding":"IEEE Transactions on Big Data 2023 12","authors":["Xovee Xu","Zhiyuan Wang","Qiang Gao","Ting Zhong","Bei Hui","Fan Zhou","Goce Trajcevski"],"abstract":"Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs. Existing models use techniques from image super-resolution and achieve good performance in FUFI. However, they often rely on supervised learning with a large amount of training data, and often lack generalization capability and face overfitting. We present a new solution: S patial- T emporal C ontrasting for Fine-Grained Urban F low Inference (STCF). It consists of (i) two pre-training networks for spatial-temporal contrasting between flow maps; and (ii) one coupled fine-tuning network for fusing learned features. By attracting spatial-temporally similar flow maps while distancing dissimilar ones within the representation space, STCF enhances efficiency and performance. Comprehensive experiments on two large-scale, real-world urban flow datasets reveal that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than prior arts.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10254322","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10254322","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatial-temporal-contrasting-for-fine-grained","repo_url":"https://github.com/Xovee/stcf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"fine-grained-urban-flow-inference","task_name":"Fine-Grained Urban Flow Inference"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P1","model":"STCF","rank_in_archive_order":1,"of":9,"metrics":{"MSE":"14.9232"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-1","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P2","model":"STCF","rank_in_archive_order":1,"of":3,"metrics":{"MSE ":"18.2566"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-2","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P3","model":"STCF","rank_in_archive_order":1,"of":2,"metrics":{"MSE":"19.4153"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-urban-flow-inference-on-taxibj-3","task":"Fine-Grained Urban Flow Inference","dataset":"TaxiBJ-P4","model":"STCF","rank_in_archive_order":1,"of":3,"metrics":{"MSE ":"11.7718"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}