{"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/an-analysis-of-human-centered-geolocation","title":"An Analysis of Human-centered Geolocation","arxiv_id":"1707.02905","date":"2017-07-10","proceeding":null,"authors":["Kaili Wang","Yu-Hui Huang","Jose Oramas","Luc van Gool","Tinne Tuytelaars"],"abstract":"Online social networks contain a constantly increasing amount of images -\nmost of them focusing on people. Due to cultural and climate factors, fashion\ntrends and physical appearance of individuals differ from city to city. In this\npaper we investigate to what extent such cues can be exploited in order to\ninfer the geographic location, i.e. the city, where a picture was taken. We\nconduct a user study, as well as an evaluation of automatic methods based on\nconvolutional neural networks. Experiments on the Fashion 144k and a\nPinterest-based dataset show that the automatic methods succeed at this task to\na reasonable extent. As a matter of fact, our empirical results suggest that\nautomatic methods can surpass human performance by a large margin. Further\ninspection of the trained models shows that human-centered characteristics,\nlike clothing style, physical features, and accessories, are informative for\nthe task at hand. Moreover, it reveals that also contextual features, e.g. wall\ntype, natural environment, etc., are taken into account by the automatic\nmethods.","url_abs":"http://arxiv.org/abs/1707.02905v3","url_pdf":"http://arxiv.org/pdf/1707.02905v3.pdf","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":"an-analysis-of-human-centered-geolocation","repo_url":"https://github.com/shadowwkl/An-Analysis-of-Human-centered-Geolocation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"an-analysis-of-human-centered-geolocation","repo_url":"https://github.com/ankur248/Fashionability-Prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}