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@InProceedings{BombonatoCamaSilv:2017:ReSiBr,
               author = "Bombonato, Leonardo and Camara-Chavez, Guillermo and Silva, 
                         Pedro",
          affiliation = "{Universidade Federal de Ouro Preto} and {Universidade Federal de 
                         Ouro Preto} and {Universidade Federal de Ouro Preto}",
                title = "Real-time single-shot brand logo recognition",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "computer vision, logo recognition, deep learning.",
             abstract = "The amount of data produced every day on theinternet increases 
                         every day and with the increasing popularityof the social networks 
                         the number of published photos arehuge, and those pictures contain 
                         several implicit or explicitbrand logos. Detecting this logos in 
                         natural images can provideinformation about how widespread is a 
                         brand, discover unwantedcopyright distribution, analyze marketing 
                         campaigns, etc. In thispaper, we propose a real-time brand logo 
                         recognition system thatoutperforms all other state-of-the-art in 
                         two different datasets.Our approach is based on the Single Shot 
                         MultiBox Detector(SSD), we explore this tool in a different domain 
                         and alsoexperiment the impact of training with pretrained weights 
                         andthe impact of warp transformations in the input images. 
                         Weconducted our experiments in two datasets, the 
                         FlickrLogos-32(FL32) and the Logos-32Plus (L32plus), which is an 
                         extension ofthe training set of the FL32. On the FL32, we 
                         outperform thestate-of-the-art by 2.5% the F-score and by 7.4% the 
                         recall. Forthe L32plus, we surpass the state-of-the-art by 1.2% 
                         the F-scoreand by 3.8% the recall.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "pt",
           targetfile = "Sibgrapi Final Version.pdf",
        urlaccessdate = "2021, Jan. 26"
}


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