Invariant recognition drives neural representations of action sequences
暂无分享,去创建一个
[1] Serge J. Belongie,et al. Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.
[2] Michael I. Jordan,et al. A more biologically plausible learning rule for neural networks. , 1991, Proceedings of the National Academy of Sciences of the United States of America.
[3] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[4] R. Vogels,et al. Functional differentiation of macaque visual temporal cortical neurons using a parametric action space. , 2009, Cerebral cortex.
[5] Joel Z. Leibo,et al. How can cells in the anterior medial face patch be viewpoint invariant , 2011 .
[6] Nikolaus Kriegeskorte,et al. Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience , 2008, Frontiers in systems neuroscience.
[7] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..
[8] Ming Yang,et al. 3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Eero P. Simoncelli,et al. How MT cells analyze the motion of visual patterns , 2006, Nature Neuroscience.
[10] Ivan Laptev,et al. On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[11] Eero P. Simoncelli,et al. A model of neuronal responses in visual area MT , 1998, Vision Research.
[12] Joel Z. Leibo,et al. Unsupervised learning of clutter-resistant visual representations from natural videos , 2014, ArXiv.
[13] Radoslaw Martin Cichy,et al. Resolving human object recognition in space and time , 2014, Nature Neuroscience.
[14] Joel Z. Leibo,et al. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex , 2014, bioRxiv.
[15] Ronen Basri,et al. Actions as space-time shapes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[16] Tomaso Poggio,et al. A fast, invariant representation for human action in the visual system. , 2016, Journal of neurophysiology.
[17] Tomaso Poggio,et al. CNS: a GPU-based framework for simulating cortically-organized networks , 2010 .
[18] H. Bülthoff,et al. Effects of temporal association on recognition memory , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[19] P. Latham,et al. Ruling out and ruling in neural codes , 2009, Proceedings of the National Academy of Sciences.
[20] P. Downing,et al. Selectivity for the human body in the fusiform gyrus. , 2005, Journal of neurophysiology.
[21] G. Johansson. Visual perception of biological motion and a model for its analysis , 1973 .
[22] Fei-Fei Li,et al. Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Joel Z. Leibo,et al. How Important Is Weight Symmetry in Backpropagation? , 2015, AAAI.
[24] Joel Z. Leibo,et al. View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation , 2016, Current Biology.
[25] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Antonio Torralba,et al. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.
[27] Sarah Wong,et al. The Mitochondrial Lon Protease Is Required for Age-Specific and Sex-Specific Adaptation to Oxidative Stress , 2017, Current Biology.
[28] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[29] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[30] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[31] Edmund T. Rolls,et al. Learning invariant object recognition in the visual system with continuous transformations , 2006, Biological Cybernetics.
[32] Eero P. Simoncelli,et al. Spatiotemporal Elements of Macaque V1 Receptive Fields , 2005, Neuron.
[33] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[34] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[35] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[36] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[37] Tomaso Poggio,et al. Learning to discount transformations as the computational goal of visual cortex , 2011 .
[38] J. Haxby,et al. fMRI Responses to Video and Point-Light Displays of Moving Humans and Manipulable Objects , 2003, Journal of Cognitive Neuroscience.
[39] Lorenzo Rosasco,et al. GURLS: a least squares library for supervised learning , 2013, J. Mach. Learn. Res..
[40] R. Blake,et al. Brain Areas Active during Visual Perception of Biological Motion , 2002, Neuron.
[41] A. J. Mistlin,et al. Visual analysis of body movements by neurones in the temporal cortex of the macaque monkey: A preliminary report , 1985, Behavioural Brain Research.
[42] James W. Davis,et al. The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[43] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[44] Thomas Serre,et al. A Biologically Inspired System for Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[45] D. Sheinberg,et al. Temporal Cortex Neurons Encode Articulated Actions as Slow Sequences of Integrated Poses , 2010, The Journal of Neuroscience.
[46] Joel Z. Leibo,et al. Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex , 2017 .
[47] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[48] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[49] Yoshua Bengio,et al. Towards Biologically Plausible Deep Learning , 2015, ArXiv.
[50] Fabio Anselmi,et al. Visual Cortex and Deep Networks: Learning Invariant Representations , 2016 .
[51] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[52] T. Poggio,et al. Cognitive neuroscience: Neural mechanisms for the recognition of biological movements , 2003, Nature Reviews Neuroscience.
[53] R. Lemon,et al. What We Know Currently about Mirror Neurons , 2013, Current Biology.
[54] Joel Z. Leibo,et al. The dynamics of invariant object recognition in the human visual system. , 2014, Journal of neurophysiology.
[55] Rémi Ronfard,et al. Free viewpoint action recognition using motion history volumes , 2006, Comput. Vis. Image Underst..
[56] Joris Vangeneugden,et al. Distinct Neural Mechanisms for Body Form and Body Motion Discriminations , 2014, The Journal of Neuroscience.
[57] Lorenzo Rosasco,et al. Unsupervised learning of invariant representations , 2016, Theor. Comput. Sci..
[58] Thomas Serre,et al. Neural representation of action sequences: how far can a simple snippet-matching model take us? , 2013, NIPS.
[59] Thomas B. Moeslund,et al. A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..
[60] John A. Pyles,et al. fMR-Adaptation Reveals Invariant Coding of Biological Motion on the Human STS , 2009, Front. Hum. Neurosci..
[61] E H Adelson,et al. Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.
[62] P. Sinha,et al. Functional neuroanatomy of biological motion perception in humans , 2001, Proceedings of the National Academy of Sciences of the United States of America.