{"id": "0b84f07af44f964817675ad961def8a51406dd2e", "paper": {"paperId": "0b84f07af44f964817675ad961def8a51406dd2e", "key": "prw", "title": "Person Re-identification in the Wild", "journal": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)", "address": "", "country": "", "address_type": "", "lat": "", "lng": "", "pdf_link": "https://arxiv.org/pdf/1604.02531.pdf", "report_link": "papers/0b84f07af44f964817675ad961def8a51406dd2e.html", "citation_count": 65, "citations_geocoded": 34, "citations_unknown": 31, "citations_empty": 1, "citations_pdf": 46, "citations_doi": 17, "name": "PRW"}, "address": null, "citations": [["ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale", "", "Microsoft", "Microsoft Corporation, Redmond, WA, USA", "One Microsoft Way, Redmond, WA 98052, USA", "47.64233180", "-122.13693020", "company", "", "United States", "2018"], ["Pose Invariant Embedding for Deep Person Re-identification", "", "University of Technology 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