Meta-Interpretive Learning from noisy images
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[1] Stephen Muggleton,et al. Learning Higher-Order Logic Programs through Abstraction and Invention , 2016, IJCAI.
[2] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[3] Trevor Darrell,et al. Natural Language Object Retrieval , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Claude Sammut,et al. Region-Based Object Categorisation Using Relational Learning , 2014, PRICAI.
[6] Stephen Muggleton,et al. Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning , 2014, ILP.
[7] Cees Snoek,et al. Attributes Make Sense on Segmented Objects , 2014, ECCV.
[8] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[9] Stephen Muggleton,et al. Bias reformulation for one-shot function induction , 2014, ECAI.
[10] David D. Cox,et al. Do we understand high-level vision? , 2014, Current Opinion in Neurobiology.
[11] Christoph H. Lampert,et al. Attribute-Based Classification for Zero-Shot Visual Object Categorization , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Stephen Muggleton,et al. MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement , 2013, ILP.
[13] Stephen Muggleton,et al. Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited , 2013, Machine Learning.
[14] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[15] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Kun Duan,et al. Discovering localized attributes for fine-grained recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Peter A. Flach,et al. ILP turns 20 , 2011, Machine Learning.
[18] Gabriela Csurka,et al. Learning structured prediction models for interactive image labeling , 2011, CVPR 2011.
[19] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[20] Tom Schrijvers,et al. Under Consideration for Publication in Theory and Practice of Logic Programming Swi-prolog , 2022 .
[21] Andrea Vedaldi,et al. Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.
[22] Geoffrey E. Hinton,et al. Zero-shot Learning with Semantic Output Codes , 2009, NIPS.
[23] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[24] G. LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[25] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[26] Gary R. Olhoeft,et al. Maximizing the information return from ground penetrating radar , 2000 .
[27] Ping-Sing Tsai,et al. Shape from Shading: A Survey , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[28] Berthold K. P. Horn. Obtaining shape from shading information , 1989 .
[29] David C. Hogg. Model-based vision: a program to see a walking person , 1983, Image Vis. Comput..
[30] Harry G. Barrow,et al. Interpreting Line Drawings as Three-Dimensional Surfaces , 1980, Artif. Intell..
[31] David L. Waltz,et al. Understanding Scene Descriptions as Event Simulations , 1980, ACL.
[32] R. Gregory. Eye and Brain: The Psychology of Seeing , 1966 .
[33] Hermann von Helmholtz,et al. Treatise on Physiological Optics , 1962 .
[34] Dan Ventura,et al. Before A Computer Can Draw, It Must First Learn To See , 2016, ICCC.
[35] Zhi-Hua Zhou,et al. Logical Vision: Meta-Interpretive Learning for Simple Geometrical Concepts , 2015, ILP.
[36] Stephen Muggleton,et al. Meta-interpretive learning: application to grammatical inference , 2013, Machine Learning.
[37] Claude Sammut,et al. Plane-based object categorisation using relational learning , 2013, Machine Learning.
[38] Katsumi Inoue,et al. ILP turns 20 - Biography and future challenges , 2012, Mach. Learn..
[39] D. A. Huffman,et al. Impossible Objects as Nonsense Sentences , 2012 .
[40] S. Rautaray,et al. Vision based hand gesture recognition for human computer interaction: a survey , 2012, Artificial Intelligence Review.
[41] Joshua B. Tenenbaum,et al. One shot learning of simple visual concepts , 2011, CogSci.
[42] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[43] Thomas Röfer,et al. B-Human Team Description for RoboCup 2007 , 2007 .
[44] Anthony G. Cohn,et al. Cognitive Vision: Integrating Symbolic Qualitative Representations with Computer Vision , 2006, Cognitive Vision Systems.