@InProceedings{AlmeidaPerValAlmPed:2021:ReLeIm,
               author = "Almeida, Lucas Barbosa de and Pereira-Ferrero, Vanessa Helena and 
                         Valem, Lucas Pascotti and Almeida, Jurandy and Pedronette, Daniel 
                         Carlos Guimar{\~a}es",
          affiliation = "UNESP  and UNESP  and UNESP  and UNIFESP  and UNESP",
                title = "Representation Learning for Image Retrieval through 3D CNN and 
                         Manifold Ranking",
            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 = "image retrieval, representation learning, manifold learning.",
             abstract = "Despite of the substantial success of Convolutional Neural 
                         Networks (CNNs) on many recognition and representation tasks, such 
                         models are very reliant on huge amount of data to allow effective 
                         training. In order to improve the generalization ability of CNNs, 
                         several approaches have been proposed, including variations of 
                         data augmentation strategies. With the goal of achieving more 
                         effective retrieval results on unsupervised learning scenarios, we 
                         propose a representation learning approach which exploits a 
                         rank-based formulation to build a more comprehensive data 
                         representation. The proposed model uses 2D and 3D CNNs trained by 
                         transfer learning and fuse both representations through a 
                         rank-based formulation based on manifold learning algorithms. Our 
                         approach was evaluated on an unsupervised image retrieval scenario 
                         applied to action recognition datasets. The experimental results 
                         indicated that significant effectiveness gains can be obtained on 
                         various datasets, reaching +56.93% of relative gains on MAP 
                         scores.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00063",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00063",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CQUPS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CQUPS",
           targetfile = "SIBGRAPI_2021_Camera_Ready.pdf",
        urlaccessdate = "2024, May 06"
}