Learning Intermediate-Level Representations of Form and Motion from Natural Movies
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[1] D. Hubel,et al. Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.
[2] J. Gibson,et al. The Senses Considered As Perceptual Systems , 1967 .
[3] H. Barrow,et al. RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES , 1978 .
[4] D. Pollen,et al. Phase relationships between adjacent simple cells in the visual cortex. , 1981, Science.
[5] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[6] 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.
[7] Bernhard Wegmann,et al. Statistical dependence between orientation filter outputs used in a human-vision-based image code , 1990, Other Conferences.
[8] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[9] Michael S. Landy,et al. Computational models of visual processing , 1991 .
[10] Michael S. Landy,et al. Nonlinear Model of Neural Responses in Cat Visual Cortex , 1991 .
[11] D. G. Albrecht,et al. Motion selectivity and the contrast-response function of simple cells in the visual cortex , 1991, Visual Neuroscience.
[12] Kechen Zhang,et al. Emergence of Position-Independent Detectors of Sense of Rotation and Dilation with Hebbian Learning: An Analysis , 1999, Neural Computation.
[13] D. V. van Essen,et al. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[14] Keiji Tanaka,et al. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.
[15] David J. Field,et al. What Is the Goal of Sensory Coding? , 1994, Neural Computation.
[16] T. Sejnowski,et al. A selection model for motion processing in area MT of primates , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[17] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[18] S. Ullman. High-Level Vision: Object Recognition and Visual Cognition , 1996 .
[19] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[20] Eero P. Simoncelli. Statistical models for images: compression, restoration and synthesis , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
[21] E. Rolls,et al. INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.
[22] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[23] J. V. van Hateren,et al. Independent component filters of natural images compared with simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[24] J. H. Hateren,et al. Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .
[25] Eero P. Simoncelli,et al. A model of neuronal responses in visual area MT , 1998, Vision Research.
[26] Julian Magarey,et al. Motion estimation using a complex-valued wavelet transform , 1998, IEEE Trans. Signal Process..
[27] Gerhard Krieger,et al. The atoms of vision: Cartesian or polar? , 1999 .
[28] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[29] I. Ohzawa,et al. Functional Micro-Organization of Primary Visual Cortex: Receptive Field Analysis of Nearby Neurons , 1999, The Journal of Neuroscience.
[30] Ramesh A. Gopinath,et al. Gaussianization , 2000, NIPS.
[31] Joshua B. Tenenbaum,et al. Separating Style and Content with Bilinear Models , 2000, Neural Computation.
[32] Aapo Hyvärinen,et al. Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.
[33] Eero P. Simoncelli,et al. Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.
[34] Aapo Hyvärinen,et al. Topographic Independent Component Analysis , 2001, Neural Computation.
[35] Y. Yamane,et al. Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns , 2001, Nature Neuroscience.
[36] Michael S. Lewicki,et al. A Model for Learning Variance Components of Natural Images , 2002, NIPS.
[37] Christoph Kayser,et al. Learning the invariance properties of complex cells from their responses to natural stimuli , 2002, The European journal of neuroscience.
[38] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[39] A. Hyvärinen,et al. A multi-layer sparse coding network learns contour coding from natural images , 2002, Vision Research.
[40] Aapo Hyvärinen,et al. Bubbles: a unifying framework for low-level statistical properties of natural image sequences. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.
[41] M. Lewicki,et al. Learning higher-order structures in natural images , 2003, Network.
[42] David J. Fleet,et al. Computation of component image velocity from local phase information , 1990, International Journal of Computer Vision.
[43] Andrea J. van Doorn,et al. The Generic Bilinear Calibration-Estimation Problem , 2004, International Journal of Computer Vision.
[44] Takeo Kanade,et al. Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.
[45] Y. LeCun,et al. Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[46] Aapo Hyvärinen,et al. Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2 , 2004, BMC Neuroscience.
[47] Rajesh P. N. Rao,et al. Bilinear Sparse Coding for Invariant Vision , 2005, Neural Computation.
[48] Laurenz Wiskott,et al. Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.
[49] Michael S. Lewicki,et al. A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals , 2005, Neural Computation.
[50] Michael S. Lewicki,et al. Is Early Vision Optimized for Extracting Higher-order Dependencies? , 2005, NIPS.
[51] D. Bradley,et al. Structure and function of visual area MT. , 2005, Annual review of neuroscience.
[52] Eero P. Simoncelli,et al. How MT cells analyze the motion of visual patterns , 2006, Nature Neuroscience.
[53] Garrison W. Cottrell,et al. Recursive ICA , 2006, NIPS.
[54] Terrence J. Sejnowski,et al. Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics , 2006, Neural Computation.
[55] Thomas Serre,et al. A Biologically Inspired System for Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[56] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[57] Geoffrey E. Hinton,et al. Unsupervised Learning of Image Transformations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[58] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[59] David D. Cox,et al. Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .
[60] Edmund T. Rolls,et al. Invariant Global Motion Recognition in the Dorsal Visual System: A Unifying Theory , 2007, Neural Computation.
[61] Aapo Hyvärinen,et al. A Two-Layer ICA-Like Model Estimated by Score Matching , 2007, ICANN.
[62] Eric T. Carlson,et al. A neural code for three-dimensional object shape in macaque inferotemporal cortex , 2008, Nature Neuroscience.
[63] Bruno A. Olshausen,et al. Learning Transformational Invariants from Natural Movies , 2008, NIPS.
[64] Guy A Orban,et al. Higher order visual processing in macaque extrastriate cortex. , 2008, Physiological reviews.
[65] Matthias Bethge,et al. The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction , 2008, NIPS.
[66] Bruno A. Olshausen,et al. Learning real and complex overcomplete representations from the statistics of natural images , 2009, Optical Engineering + Applications.
[67] Richard E. Turner,et al. A Structured Model of Video Reproduces Primary Visual Cortical Organisation , 2009, PLoS Comput. Biol..
[68] Eero P. Simoncelli,et al. Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization , 2009, Neural Computation.
[69] Bruno A. Olshausen,et al. Learning transport operators for image manifolds , 2009, NIPS.
[70] Michael S. Lewicki,et al. Emergence of complex cell properties by learning to generalize in natural scenes , 2009, Nature.
[71] Charles F. Cadieu,et al. Phase Coupling Estimation from Multivariate Phase Statistics , 2009, Neural Computation.
[72] Charles F. Cadieu,et al. Modeling Image Structure with Factorized Phase-Coupled Boltzmann Machines , 2010, ArXiv.
[73] Haim Sompolinsky,et al. Bayesian model of dynamic image stabilization in the visual system , 2010, Proceedings of the National Academy of Sciences.
[74] Geoffrey E. Hinton,et al. Modeling pixel means and covariances using factorized third-order boltzmann machines , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[75] Jordan W. Suchow,et al. Motion Silences Awareness of Visual Change , 2011, Current Biology.
[76] H. B. Barlow,et al. Possible Principles Underlying the Transformations of Sensory Messages , 2012 .
[77] Christopher J. Rozell,et al. A Common Network Architecture Efficiently Implements a Variety of Sparsity-Based Inference Problems , 2012, Neural Computation.
[78] Xiaoyuan Zhu,et al. Multi-Scale Spatial Concatenations of Local Features in Natural Scenes and Scene Classification , 2013, PloS one.
[79] Aapo Hyvärinen,et al. A three-layer model of natural image statistics , 2013, Journal of Physiology-Paris.
[80] Roland Memisevic,et al. Learning to Relate Images , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[81] Simon Haykin,et al. Improved Sparse Coding Under the Influence of Perceptual Attention , 2014, Neural Computation.
[82] R. K. Simpson. Nature Neuroscience , 2022 .