@InProceedings{DouradoNetoGuthCampWeig:2021:DoAdHo,
               author = "Dourado Neto, Aloisio and Guth, Frederico and Campos, Teofilo de 
                         and Weigang, Li",
          affiliation = "{Universidade de Bras{\'{\i}}lia } and {Universidade de 
                         Bras{\'{\i}}lia } and {Universidade de Bras{\'{\i}}lia } and 
                         {Universidade de Bras{\'{\i}}lia}",
                title = "Domain Adaptation for Holistic Skin Detection",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "computer vision, deep learning, semantic segmentation, skin 
                         detection, domain adaptation.",
             abstract = "Human skin detection in images is a widely studied topic of 
                         Computer Vision for which it is commonly accepted that analysis of 
                         pixel color or local patches may suffice. However, we found that 
                         the lack of contextual information may hinder the performance of 
                         local approaches. In this paper, we present a comprehensive 
                         evaluation of holistic and local Convolutional Neural Network 
                         (CNN) approaches on in-domain and cross-domain experiments and 
                         compare them with state-of-the-art pixel-based approaches. We also 
                         propose combining inductive transfer learning and unsupervised 
                         domain adaptation methods evaluated on different domains under 
                         several amounts of labelled data availability. We show a clear 
                         superiority of CNN over pixel-based approaches even without 
                         labeled training samples on the target domain and provide 
                         experimental support for the superiority of holistic over local 
                         approaches for human skin detection.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00056",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00056",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CKLG2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CKLG2",
           targetfile = "
                         
                         SIBGRAP_paper_39__Domain_Adaptation_for_Holistic_Skin_Detection.pdf",
        urlaccessdate = "2024, May 06"
}