Object Recognition with Hidden Attributes
暂无分享,去创建一个
[1] Xiaoyang Wang,et al. Attribute Augmentation with Sparse Coding , 2014, 2014 22nd International Conference on Pattern Recognition.
[2] Kristen Grauman,et al. Interactively building a discriminative vocabulary of nameable attributes , 2011, CVPR 2011.
[3] Y. Takane,et al. Generalized Inverse Matrices , 2011 .
[4] Michael Felsberg,et al. 22nd International Conference on Pattern Recognition (ICPR) , 2014 .
[5] Vladimir Vapnik,et al. A new learning paradigm: Learning using privileged information , 2009, Neural Networks.
[6] Andrew Zisserman,et al. Advances in Neural Information Processing Systems (NIPS) , 2007 .
[7] Zhi-Hua Zhou,et al. Semi-supervised learning by disagreement , 2010, Knowledge and Information Systems.
[8] Luc Van Gool,et al. European conference on computer vision (ECCV) , 2006, eccv 2006.
[9] Qiang Ji,et al. A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects , 2013, 2013 IEEE International Conference on Computer Vision.
[10] Kristen Grauman,et al. Relative attributes , 2011, 2011 International Conference on Computer Vision.
[11] Qiang Ji,et al. A novel probabilistic approach utilizing clip attribute as hidden knowledge for event recognition , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[12] Andrew Zisserman,et al. Learning Visual Attributes , 2007, NIPS.
[13] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[14] Yang Wang,et al. A Discriminative Latent Model of Object Classes and Attributes , 2010, ECCV.
[15] Lawrence Carin,et al. Semi-Supervised Classification , 2004, Encyclopedia of Database Systems.
[16] Vikas Sindhwani,et al. An RKHS for multi-view learning and manifold co-regularization , 2008, ICML '08.
[17] Ali Farhadi,et al. Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Dima Damen,et al. Recognizing linked events: Searching the space of feasible explanations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Qiang Ji,et al. Learning with Hidden Information , 2014, 2014 22nd International Conference on Pattern Recognition.
[20] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[21] Adriana Kovashka,et al. Actively selecting annotations among objects and attributes , 2011, 2011 International Conference on Computer Vision.
[22] Mikhail Belkin,et al. A Co-Regularization Approach to Semi-supervised Learning with Multiple Views , 2005 .
[23] R. Penrose. A Generalized inverse for matrices , 1955 .
[24] Yue Zhao,et al. Shared speech attribute augmentation for English-Tibetan cross-language phone recognition , 2015, 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).
[25] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[26] Christoph H. Lampert,et al. Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[27] Christoph H. Lampert,et al. Learning to Rank Using Privileged Information , 2013, 2013 IEEE International Conference on Computer Vision.
[28] Cordelia Schmid,et al. Label-Embedding for Attribute-Based Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[29] V. Buchstaber,et al. Mathematical Proceedings of the Cambridge Philosophical Society , 1979 .
[30] S. Maybank,et al. Knowledge and Information Systems REGULAR PAPER , 2006 .
[31] John Shawe-Taylor,et al. Two view learning: SVM-2K, Theory and Practice , 2005, NIPS.
[32] Kristen Grauman,et al. Sharing features between objects and their attributes , 2011, CVPR 2011.