From Red Wine to Red Tomato: Composition with Context
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[1] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[2] Xu Wei,et al. Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[3] Jonghyun Choi,et al. Adding Unlabeled Samples to Categories by Learned Attributes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[4] Ulf Grenander,et al. General Pattern Theory: A Mathematical Study of Regular Structures , 1993 .
[5] Song-Chun Zhu,et al. Learning AND-OR Templates for Object Recognition and Detection , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[7] Ernest Valveny,et al. Word Spotting and Recognition with Embedded Attributes , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[9] Deva Ramanan,et al. Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery , 2016, ArXiv.
[10] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[11] Fei-Fei Li,et al. Shifting Weights: Adapting Object Detectors from Image to Video , 2012, NIPS.
[12] 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.
[13] Babak Saleh,et al. Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions , 2013, 2013 IEEE International Conference on Computer Vision.
[14] Cees Snoek,et al. COSTA: Co-Occurrence Statistics for Zero-Shot Classification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[15] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[16] Sebastian Thrun,et al. Learning One More Thing , 1994, IJCAI.
[17] Yi Yang,et al. Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.
[18] Zhuowen Tu,et al. Image Parsing: Unifying Segmentation, Detection, and Recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[19] Ali Farhadi,et al. Visalogy: Answering Visual Analogy Questions , 2015, NIPS.
[20] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[21] Ali Farhadi,et al. Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Ali Farhadi,et al. Recognition using visual phrases , 2011, CVPR 2011.
[23] J. Fodor,et al. Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.
[24] Trevor Darrell,et al. YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition , 2013, 2013 IEEE International Conference on Computer Vision.
[25] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[26] Kate Saenko,et al. Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild , 2014, COLING.
[27] Joshua B. Tenenbaum,et al. Concept learning as motor program induction: A large-scale empirical study , 2012, CogSci.
[28] Dan Klein,et al. Neural Module Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] ZissermanAndrew,et al. The Pascal Visual Object Classes Challenge , 2015 .
[30] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[31] James Hays,et al. COCO Attributes: Attributes for People, Animals, and Objects , 2016, ECCV.
[32] Donald D. Hoffman,et al. Parts of recognition , 1984, Cognition.
[33] Song-Chun Zhu,et al. A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs , 2011, International Journal of Computer Vision.
[34] Yifei Lu,et al. Max Margin AND/OR Graph learning for parsing the human body , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[35] Joshua B. Tenenbaum,et al. One shot learning of simple visual concepts , 2011, CogSci.
[36] Bernt Schiele,et al. Learning Deep Representations of Fine-Grained Visual Descriptions , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Edward H. Adelson,et al. Discovering states and transformations in image collections , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Barbara Caputo,et al. Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[39] Sanja Fidler,et al. Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[40] Samy Bengio,et al. Large-Scale Object Classification Using Label Relation Graphs , 2014, ECCV.
[41] Philip H. S. Torr,et al. An embarrassingly simple approach to zero-shot learning , 2015, ICML.
[42] Michael S. Bernstein,et al. Visual Relationship Detection with Language Priors , 2016, ECCV.
[43] Feng Han,et al. Bottom-Up/Top-Down Image Parsing with Attribute Grammar , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] Ross B. Girshick,et al. Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Martial Hebert,et al. Learning to Learn: Model Regression Networks for Easy Small Sample Learning , 2016, ECCV.
[46] G. Frege. On Sense and Reference , 1948 .
[47] Jianguo Zhang,et al. The PASCAL Visual Object Classes Challenge , 2006 .
[48] Armand Joulin,et al. Deep Fragment Embeddings for Bidirectional Image Sentence Mapping , 2014, NIPS.
[49] David A. McAllester,et al. A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[50] Rich Caruana,et al. Multitask Learning , 1997, Machine-mediated learning.
[51] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[52] Roozbeh Mottaghi,et al. Complexity of Representation and Inference in Compositional Models with Part Sharing , 2013, J. Mach. Learn. Res..
[53] Kristen Grauman,et al. Decorrelating Semantic Visual Attributes by Resisting the Urge to Share , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[54] Pietro Perona,et al. One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] William T. Freeman,et al. Latent hierarchical structural learning for object detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[56] Venkatesh Saligrama,et al. Zero-Shot Learning via Semantic Similarity Embedding , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[57] Cordelia Schmid,et al. Label-Embedding for Attribute-Based Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[58] Marc'Aurelio Ranzato,et al. DeViSE: A Deep Visual-Semantic Embedding Model , 2013, NIPS.
[59] Long Zhu,et al. Recursive Compositional Models for Vision: Description and Review of Recent Work , 2011, Journal of Mathematical Imaging and Vision.
[60] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[61] Geoffrey E. Hinton,et al. A Simple Way to Initialize Recurrent Networks of Rectified Linear Units , 2015, ArXiv.
[62] I. Biederman. Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.
[63] Erik G. Learned-Miller,et al. Building a classification cascade for visual identification from one example , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[64] Michael Fink,et al. Object Classification from a Single Example Utilizing Class Relevance Metrics , 2004, NIPS.
[65] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[66] Kristen Grauman,et al. Relative attributes , 2011, 2011 International Conference on Computer Vision.
[67] Dan Klein,et al. Learning to Compose Neural Networks for Question Answering , 2016, NAACL.
[68] Markus Diesmann,et al. Compositionality in neural control: an interdisciplinary study of scribbling movements in primates , 2013, Front. Comput. Neurosci..