Search for: visual-attributes
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    The visual object tracking VOT2013 challenge results

    , Article Proceedings of the IEEE International Conference on Computer Vision ; 2013 , Pages 98-111 ; 9781479930227 (ISBN) Kristan, M ; Pflugfelder, R ; Leonardis, A ; Matas, J ; Porikli, F ; Čehovin, L ; Nebehay, G ; Fernandez, G ; Vojíř, T ; Gatt, A ; Khajenezhad, A ; Salahledin, A ; Soltani-Farani, A ; Zarezade, A ; Petrosino, A ; Milton, A ; Bozorgtabar, B ; Li, B ; Chan, C. S ; Heng, C ; Ward, D ; Kearney, D ; Monekosso, D ; Karaimer, H. C ; Rabiee, H. R ; Zhu, J ; Gao, J ; Xiao, J ; Zhang, J ; Xing, J ; Huang, K ; Lebeda, K ; Cao, L ; Maresca, M. E ; Lim, M. K ; ELHelw, M ; Felsberg, M ; Remagnino, P ; Bowden, R ; Goecke, R ; Stolkin, R ; Lim, S. Y. Y ; Maher, S ; Poullot, S ; Wong, S ; Satoh, S ; Chen, W ; Hu, W ; Zhang, X ; Li, Y ; Niu, Z ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2013
    Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply... 

    Deep relative attributes

    , Article 13th Asian Conference on Computer Vision, ACCV 2016, 20 November 2016 through 24 November 2016 ; Volume 10115 LNCS , 2017 , Pages 118-133 ; 03029743 (ISSN); 9783319541921 (ISBN) Souri, Y ; Noury, E ; Adeli, E ; Sharif University of Technology
    Springer Verlag  2017
    Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an...