Unsupervised Visual Representation Learning by Context Prediction
暂无分享,去创建一个
[1] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[2] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[3] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[4] Pietro Perona,et al. Unsupervised Learning of Models for Recognition , 2000, ECCV.
[5] Edward H. Adelson,et al. On seeing stuff: the perception of materials by humans and machines , 2001, IS&T/SPIE Electronic Imaging.
[6] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[7] Tong Zhang,et al. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , 2005, J. Mach. Learn. Res..
[8] Antonio Torralba,et al. Learning hierarchical models of scenes, objects, and parts , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[9] Alexei A. Efros,et al. Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[10] Alexei A. Efros,et al. Using Multiple Segmentations to Discover Objects and their Extent in Image Collections , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[11] Trevor Darrell,et al. Unsupervised Learning of Categories from Sets of Partially Matching Image Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[12] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[13] Rajat Raina,et al. Efficient sparse coding algorithms , 2006, NIPS.
[14] Jun'ichi Tsujii,et al. A discriminative language model with pseudo-negative samples , 2007, ACL.
[15] Michael Isard,et al. Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[16] Christos Faloutsos,et al. Unsupervised modeling of object categories using link analysis techniques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Yiannis Aloimonos,et al. Who killed the directed model? , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[19] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[20] Luc Van Gool,et al. World-scale mining of objects and events from community photo collections , 2008, CIVR '08.
[21] Stephen Parkinson,et al. A Treatise on Optics , 2008 .
[22] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[23] Yong Jae Lee,et al. Foreground Focus: Unsupervised Learning from Partially Matching Images , 2009, International Journal of Computer Vision.
[24] O. Chum,et al. Geometric min-Hashing: Finding a (thick) needle in a haystack , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Alexei A. Efros,et al. Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships , 2009, NIPS.
[26] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[27] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[28] Jia Deng,et al. A large-scale hierarchical image database , 2009, CVPR 2009.
[29] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Sinisa Todorovic,et al. From a Set of Shapes to Object Discovery , 2010, ECCV.
[31] Leonidas J. Guibas,et al. Image webs: Computing and exploiting connectivity in image collections , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[32] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[33] Hugo Larochelle,et al. The Neural Autoregressive Distribution Estimator , 2011, AISTATS.
[34] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[35] Alexei A. Efros,et al. Unsupervised Discovery of Mid-Level Discriminative Patches , 2012, ECCV.
[36] Alexei A. Efros,et al. What makes Paris look like Paris? , 2015, Commun. ACM.
[37] Koen E. A. van de Sande,et al. Selective Search for Object Recognition , 2013, International Journal of Computer Vision.
[38] Zhuowen Tu,et al. Harvesting Mid-level Visual Concepts from Large-Scale Internet Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[39] Cordelia Schmid,et al. Segmentation Driven Object Detection with Fisher Vectors , 2013, 2013 IEEE International Conference on Computer Vision.
[40] C. V. Jawahar,et al. Blocks That Shout: Distinctive Parts for Scene Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[41] Marc'Aurelio Ranzato,et al. Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[42] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[43] Derek Hoiem,et al. Learning Collections of Part Models for Object Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[44] Alexei A. Efros,et al. Mid-level Visual Element Discovery as Discriminative Mode Seeking , 2013, NIPS.
[45] Jean Ponce,et al. Learning Discriminative Part Detectors for Image Classification and Cosegmentation , 2013, 2013 IEEE International Conference on Computer Vision.
[46] Martial Hebert,et al. Data-Driven 3D Primitives for Single Image Understanding , 2013, 2013 IEEE International Conference on Computer Vision.
[47] Ming Yang,et al. Regionlets for Generic Object Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[48] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[49] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[50] Alexei A. Efros,et al. Context as Supervisory Signal: Discovering Objects with Predictable Context , 2014, ECCV.
[51] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[52] Yoshua Bengio,et al. Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.
[53] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[54] Jitendra Malik,et al. Analyzing the Performance of Multilayer Neural Networks for Object Recognition , 2014, ECCV.
[55] Björn Ommer,et al. Randomized Max-Margin Compositions for Visual Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[56] Michal Irani,et al. “Clustering by Composition”—Unsupervised Discovery of Image Categories , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[58] Frank Dellaert,et al. Dataset fingerprints: Exploring image collections through data mining , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Nitish Srivastava. Unsupervised Learning of Visual Representations using Videos , 2015 .
[60] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[61] David A. Shamma,et al. The New Data and New Challenges in Multimedia Research , 2015, ArXiv.
[62] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[63] Matthias Bethge,et al. Generative Image Modeling Using Spatial LSTMs , 2015, NIPS.
[64] Trevor Darrell,et al. Data-dependent Initializations of Convolutional Neural Networks , 2015, ICLR.
[65] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.