Towards a Visual Story Network Using Multiple Views for Object Recognition at Different Levels of Spatiotemporal Context
暂无分享,去创建一个
[1] D. George,et al. A hierarchical Bayesian model of invariant pattern recognition in the visual cortex , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[2] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[3] David G. Lowe,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.
[4] Yanxi Liu,et al. Online Selection of Discriminative Tracking Features , 2005, IEEE Trans. Pattern Anal. Mach. Intell..
[5] Antonio Torralba,et al. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.
[6] Yoram Singer,et al. The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.
[7] Martial Hebert,et al. Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Pascal Vincent,et al. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.
[9] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[10] 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).
[11] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[12] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[14] Cyrus Rashtchian,et al. Every Picture Tells a Story: Generating Sentences from Images , 2010, ECCV.
[15] S. Grossberg. The complementary brain: unifying brain dynamics and modularity , 2000, Trends in Cognitive Sciences.
[16] Zhuowen Tu,et al. Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Cordelia Schmid,et al. Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.
[18] S. Grossberg,et al. View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds , 2009, Cognitive Psychology.
[19] Trevor Darrell,et al. The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[20] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[21] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[22] Geoffrey E. Hinton,et al. Transforming Auto-Encoders , 2011, ICANN.
[23] N. Sigala,et al. Visual categorization shapes feature selectivity in the primate temporal cortex , 2002, Nature.
[24] Qiang Chen,et al. Contextualizing Object Detection and Classification , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Finn V. Jensen,et al. Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.
[27] Martial Hebert,et al. Unsupervised Learning for Graph Matching , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[28] S. Grossberg,et al. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.
[29] Antonio Torralba,et al. Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.
[30] Andrea Vedaldi,et al. Objects in Context , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[31] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[32] Rahul Sukthankar,et al. Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision , 2015, AAAI.
[33] E. Warrington,et al. Visual associative agnosia: a clinico-anatomical study of a single case. , 1986, Journal of neurology, neurosurgery, and psychiatry.
[34] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[35] S. Grossberg. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control , 2015, Brain Research.
[36] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] J. Rowsell,et al. Artifactual Literacies: Every Object Tells a Story , 2010 .
[38] F. Michael Connelly,et al. Stories of Experience and Narrative Inquiry , 1990 .
[39] R. Schank,et al. Knowledge and Memory: The Real Story , 1995 .
[40] Stephen Grossberg,et al. A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..
[41] Cristian Sminchisescu,et al. Generalized Boundaries from Multiple Image Interpretations , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Elizabeth K. Warrington,et al. Visual Apperceptive Agnosia: A Clinico-Anatomical Study of Three Cases , 1988, Cortex.
[43] Peter J. Rousseeuw,et al. Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .
[44] G. Edelman,et al. The Mindful Brain: Cortical Organization and the Group-Selective Theory of Higher Brain Function , 1978 .
[45] Cordelia Schmid,et al. DeepFlow: Large Displacement Optical Flow with Deep Matching , 2013, 2013 IEEE International Conference on Computer Vision.
[46] Mario Vento,et al. Thirty Years Of Graph Matching In Pattern Recognition , 2004, Int. J. Pattern Recognit. Artif. Intell..
[47] Cristian Sminchisescu,et al. The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[48] Sanja Fidler,et al. Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[49] Cordelia Schmid,et al. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[50] Martial Hebert,et al. A spectral technique for correspondence problems using pairwise constraints , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[51] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[52] John R. Koza,et al. Genetic Programming as a Darwinian Invention Machine , 1999, EuroGP.
[53] Qiang Chen,et al. Hierarchical matching with side information for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[54] Yoshua Bengio,et al. Maxout Networks , 2013, ICML.
[55] S. Grossberg,et al. Where’s Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene , 2014, Front. Integr. Neurosci..
[56] Geoffrey E. Hinton,et al. Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines , 2010, Neural Computation.
[57] Hrishikesh B. Aradhye,et al. Video2Text: Learning to Annotate Video Content , 2009, 2009 IEEE International Conference on Data Mining Workshops.
[58] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[59] Jitendra Malik,et al. Shape Context: A New Descriptor for Shape Matching and Object Recognition , 2000, NIPS.
[60] Trevor Darrell,et al. Hidden Conditional Random Fields , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[61] Charless C. Fowlkes,et al. Discriminative Models for Multi-Class Object Layout , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[62] Stephen Grossberg,et al. Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world , 2013, Neural Networks.
[63] Aaron C. Koralek,et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills , 2012, Nature.
[64] Alexei A. Efros,et al. Putting Objects in Perspective , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[65] Pascal Vincent,et al. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.
[66] Pascal Vincent,et al. Higher Order Contractive Auto-Encoder , 2011, ECML/PKDD.
[67] Lei Wang,et al. AdaBoost with SVM-based component classifiers , 2008, Eng. Appl. Artif. Intell..
[68] Martial Hebert,et al. Smoothing-based Optimization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[69] Rahul Sukthankar,et al. Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition , 2014, ArXiv.
[70] A. Torralba,et al. The role of context in object recognition , 2007, Trends in Cognitive Sciences.