Zero-Shot Recognition via Structured Prediction
暂无分享,去创建一个
[1] Kristen Grauman,et al. Interactively building a discriminative vocabulary of nameable attributes , 2011, CVPR 2011.
[2] Ali Farhadi,et al. Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[3] Alexander C. Berg,et al. Automatic Attribute Discovery and Characterization from Noisy Web Data , 2010, ECCV.
[4] Shaogang Gong,et al. Unsupervised Domain Adaptation for Zero-Shot Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[5] Xiaodong Yu,et al. Attribute-Based Transfer Learning for Object Categorization with Zero/One Training Example , 2010, ECCV.
[6] Sanja Fidler,et al. Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[7] Pietro Perona,et al. The Caltech-UCSD Birds-200-2011 Dataset , 2011 .
[8] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[9] Shih-Fu Chang,et al. Designing Category-Level Attributes for Discriminative Visual Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[10] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[11] Bernt Schiele,et al. Evaluating knowledge transfer and zero-shot learning in a large-scale setting , 2011, CVPR 2011.
[12] Juhan Nam,et al. Multimodal Deep Learning , 2011, ICML.
[13] Christoph H. Lampert,et al. Attribute-Based Classification for Zero-Shot Visual Object Categorization , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Philip H. S. Torr,et al. An embarrassingly simple approach to zero-shot learning , 2015, ICML.
[15] Vinod Nair,et al. A joint learning framework for attribute models and object descriptions , 2011, 2011 International Conference on Computer Vision.
[16] Dale Schuurmans,et al. Semi-Supervised Zero-Shot Classification with Label Representation Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[17] Geoffrey E. Hinton,et al. Zero-shot Learning with Semantic Output Codes , 2009, NIPS.
[18] Xin Li,et al. Max-Margin Zero-Shot Learning for Multi-class Classification , 2015, AISTATS.
[19] Andrew Y. Ng,et al. Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.
[20] Venkatesh Saligrama,et al. Zero-Shot Learning via Semantic Similarity Embedding , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[21] Cordelia Schmid,et al. Label-Embedding for Attribute-Based Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Marc'Aurelio Ranzato,et al. DeViSE: A Deep Visual-Semantic Embedding Model , 2013, NIPS.
[23] Tommi S. Jaakkola,et al. Tutorial on variational approximation methods , 2000 .
[24] Prateek Jain,et al. Sparse Local Embeddings for Extreme Multi-label Classification , 2015, NIPS.
[25] Jeff A. Bilmes,et al. On Deep Multi-View Representation Learning , 2015, ICML.
[26] Venkatesh Saligrama,et al. Zero-Shot Learning via Joint Latent Similarity Embedding , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[28] Thomas Hofmann,et al. Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.
[29] Fei Wang,et al. Label Propagation through Linear Neighborhoods , 2008, IEEE Trans. Knowl. Data Eng..
[30] Bernt Schiele,et al. Evaluation of output embeddings for fine-grained image classification , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Chen Xu,et al. The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding , 2014, International Journal of Computer Vision.
[32] XiangTao,et al. Transductive Multi-View Zero-Shot Learning , 2015 .
[33] Samy Bengio,et al. Zero-Shot Learning by Convex Combination of Semantic Embeddings , 2013, ICLR.
[34] Yongxin Yang,et al. A Unified Perspective on Multi-Domain and Multi-Task Learning , 2014, ICLR.
[35] Qiang Ji,et al. A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects , 2013, 2013 IEEE International Conference on Computer Vision.
[36] Christoph H. Lampert,et al. CoConut: Co-Classification with Output Space Regularization , 2014, BMVC.
[37] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[38] S. V. N. Vishwanathan,et al. Efficient max-margin multi-label classification with applications to zero-shot learning , 2012, Machine Learning.
[39] Cees Snoek,et al. COSTA: Co-Occurrence Statistics for Zero-Shot Classification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[40] C. Lawrence Zitnick,et al. Zero-Shot Learning via Visual Abstraction , 2014, ECCV.
[41] Christoph H. Lampert,et al. Co-Classification with Output Space Regularization , 2014, BMVC 2014.
[42] Gabriela Csurka,et al. Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost , 2012, ECCV.
[43] Mohammad H. Poursaeidi,et al. Robust support vector machines for multiple instance learning , 2014, Ann. Oper. Res..