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Diffstat (limited to 'site/datasets/citations/apis.json')
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1 files changed, 1 insertions, 1 deletions
diff --git a/site/datasets/citations/apis.json b/site/datasets/citations/apis.json index 365ddea3..949f94d5 100644 --- a/site/datasets/citations/apis.json +++ b/site/datasets/citations/apis.json @@ -1 +1 @@ -{"id": "488e475eeb3bb39a145f23ede197cd3620f1d98a", "paper": {"paperId": "488e475eeb3bb39a145f23ede197cd3620f1d98a", "key": "apis", "title": "Pedestrian Attribute Classification in Surveillance: Database and Evaluation", "journal": "2013 IEEE International Conference on Computer Vision Workshops", "address": "", "address_type": "", "lat": "", "lng": "", "pdf_link": "http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W10/papers/Zhu_Pedestrian_Attribute_Classification_2013_ICCV_paper.pdf", "report_link": "papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html", "citation_count": 26, "citations_geocoded": 10, "citations_unknown": 16, "citations_empty": 1, "citations_pdf": 13, "citations_doi": 13, "name": "APiS1.0"}, "address": null, "citations": [["Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model", "", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", "", "2017"], ["Pedestrian Attribute Recognition At Far Distance", "", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", "", 2014], ["Analysing Soft Clothing Biometrics for Retrieval", "", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", "", 2014], ["Pedestrian Attribute Detection Using CNN", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", "", 2016], ["Attribute Recognition by Joint Recurrent Learning of Context and Correlation", "", "Queen Mary University of London", "Queen Mary University of London", "Queen Mary (University of London), Mile End Road, Globe Town, Mile End, London Borough of Tower Hamlets, London, Greater London, England, E1 4NS, UK", "51.52472720", "-0.03931035", "edu", "", "2017"], ["Deep domain adaptation for describing people based on fine-grained clothing attributes", "", "IBM Research, North Carolina", "IBM Research", "IBM, East Cornwallis Road, Research Triangle Park, Nelson, Durham County, North Carolina, 27709, USA", "35.90422720", "-78.85565763", "company", "", 2015], ["A Richly Annotated Dataset for Pedestrian Attribute Recognition", "", "Temple University", "Temple University", "Temple University School of Podiatric Medicine, Race Street, Chinatown, Philadelphia, Philadelphia County, Pennsylvania, 19103, USA", "39.95472495", "-75.15346905", "edu", "", 2016], ["Soft Biometric Recognition from Comparative Crowdsourced Annotations", "", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", "", 2015], ["Analysing comparative soft biometrics from crowdsourced annotations", "", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", "", 2016], ["Improve Pedestrian Attribute Classification by Weighted Interactions from Other Attributes", "", "Chinese Academy of Sciences", "Chinese Academy of Sciences", "\u4e2d\u56fd\u79d1\u5b66\u9662\u5fc3\u7406\u7814\u7a76\u6240, 16, \u6797\u8403\u8def, \u671d\u9633\u533a / Chaoyang, \u5317\u4eac\u5e02, 100101, \u4e2d\u56fd", "40.00447950", "116.37023800", "edu", "", 2014]]}
\ No newline at end of file +{"id": "488e475eeb3bb39a145f23ede197cd3620f1d98a", "paper": {"paperId": "488e475eeb3bb39a145f23ede197cd3620f1d98a", "key": "apis", "title": "Pedestrian Attribute Classification in Surveillance: Database and Evaluation", "journal": "2013 IEEE International Conference on Computer Vision Workshops", "address": "", "address_type": "", "lat": "", "lng": "", "pdf_link": "http://www.cbsr.ia.ac.cn/english/APiS_1.0_paper.pdf", "report_link": "papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html", "citation_count": 26, "citations_geocoded": 17, "citations_unknown": 9, "citations_empty": 1, "citations_pdf": 13, "citations_doi": 13, "name": "APiS1.0"}, "address": null, "citations": [["Person re-identification using CNN features learned from combination of attributes", "Faculty of Information Science and Electrical Engineering (ISEE), Kyushu University, Japan", "Kyushu University", "Kyushu University", "\u4f0a\u90fd\u30b2\u30b9\u30c8\u30cf\u30a6\u30b9, \u685c\u4e95\u592a\u90ce\u4e38\u7dda, \u897f\u533a, \u798f\u5ca1\u5e02, \u798f\u5ca1\u770c, \u4e5d\u5dde\u5730\u65b9, 819\u22120395, \u65e5\u672c", "33.59914655", "130.22359848", "edu", "", "2016"], ["Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model", "", "Karlsruhe Institute of Technology", "Karlsruhe Institute of Technology", "KIT, Leopoldshafener Allee, Linkenheim, Linkenheim-Hochstetten, Landkreis Karlsruhe, Regierungsbezirk Karlsruhe, Baden-W\u00fcrttemberg, 76351, Deutschland", "49.10184375", "8.43312560", "edu", "", "2017"], ["Pedestrian Attribute Recognition At Far Distance", "", "Chinese University of Hong Kong", "The Chinese University of Hong Kong", "\u4e2d\u5927 CUHK, NA\u68af New Asia Stairs, \u99ac\u6599\u6c34 Ma Liu Shui, \u4e5d\u809a\u6751 Kau To Village, \u6c99\u7530\u5340 Sha Tin District, \u65b0\u754c New Territories, HK, DD193 1191, \u4e2d\u56fd", "22.42031295", "114.20788644", "edu", "", "2014"], ["Analysing Soft Clothing Biometrics for Retrieval", "", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", "", "2014"], ["Pedestrian gender classification using combined global and local parts-based convolutional neural networks", "Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia", "Universiti Tunku Abdul Rahman", "Universiti Tunku Abdul Rahman", "Jalan Universiti, Bandar Barat, 31900 Kampar, Negeri Perak, Malaysia", "4.34006730", "101.14297990", "edu", "", "2018"], ["Learning to Recognize Pedestrian Attribute", "", "Member", "Member", "1322 N Inglewood Ave, Coffeyville, KS 67337, USA", "37.05826350", "-95.67914910", "edu", "", "2015"], ["Pedestrian Attribute Detection using CNN", "", "Stanford University", "Stanford University", "Stanford University, Memorial Way, Stanford, Santa Clara County, California, 94305-6015, USA", "37.43131385", "-122.16936535", "edu", "", "2016"], ["From Clothing to Identity: Manual and Automatic Soft Biometrics", "School of Electronics and Computer Science, University of Southampton, Southampton, U.K.", "University of Southampton", "University of Southampton", "Waterfront Campus, European Way, Port of Southampton, St Mary's, Southampton, South East, England, SO14 3JW, UK", "50.89273635", "-1.39464295", "edu", "", "2016"], ["Attribute Recognition by Joint Recurrent Learning of Context and Correlation", "", "Queen Mary University of London", "Queen Mary University of London", "Queen Mary (University of London), Mile End Road, Globe Town, Mile End, London Borough of Tower Hamlets, London, Greater London, England, E1 4NS, UK", "51.52472720", "-0.03931035", "edu", "", "2017"], ["Deep domain adaptation for describing people based on fine-grained clothing attributes", "", "IBM Research, North Carolina", "IBM Research", "IBM, East Cornwallis Road, Research Triangle Park, Nelson, Durham County, North Carolina, 27709, USA", "35.90422720", "-78.85565763", "company", "", "2015"], ["A hybrid approach to pedestrian clothing color attribute extraction", "OMRON Social Solutions Co. 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