The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex
暂无分享,去创建一个
[1] Yann LeCun,et al. Handwritten zip code recognition with multilayer networks , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.
[2] M. Tarr,et al. Do viewpoint-dependent mechanisms generalize across members of a class? , 1998, Cognition.
[3] Russell A. Epstein,et al. Scene Areas in Humans and Macaques , 2013, Neuron.
[4] P. Downing,et al. The role of occipitotemporal body-selective regions in person perception , 2011, Cognitive neuroscience.
[5] J. Keenan,et al. Lesions of the fusiform face area impair perception of facial configuration in prosopagnosia , 2002, Neurology.
[6] Edmund T. Rolls,et al. Deformation-specific and deformation-invariant visual object recognition: pose vs. identity recognition of people and deforming objects , 2014, Front. Comput. Neurosci..
[7] H. Barlow. Why have multiple cortical areas? , 1986, Vision Research.
[8] R. Yin. Looking at Upside-down Faces , 1969 .
[9] Joel Z. Leibo,et al. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? , 2013, ArXiv.
[10] N. Logothetis,et al. fMRI of the Face-Processing Network in the Ventral Temporal Lobe of Awake and Anesthetized Macaques , 2011, Neuron.
[11] H H Bülthoff,et al. Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[12] I. Gauthier,et al. Computational approaches to the development of perceptual expertise , 2004, Trends in Cognitive Sciences.
[13] D. Hubel,et al. Uniformity of monkey striate cortex: A parallel relationship between field size, scatter, and magnification factor , 1974, The Journal of comparative neurology.
[14] Kevan A. C. Martin,et al. A Canonical Microcircuit for Neocortex , 1989, Neural Computation.
[15] N. Kanwisher,et al. How Distributed Is Visual Category Information in Human Occipito-Temporal Cortex? An fMRI Study , 2002, Neuron.
[16] A. Oliva,et al. A Real-World Size Organization of Object Responses in Occipitotemporal Cortex , 2012, Neuron.
[17] M. Sigman,et al. Opinion TRENDS in Cognitive Sciences Vol.9 No.7 July 2005 The neural code for written words: a proposal , 2022 .
[18] Xueqi Cheng,et al. A Network for Scene Processing in the Macaque Temporal Lobe , 2013, Neuron.
[19] Elias B. Issa,et al. Precedence of the Eye Region in Neural Processing of Faces , 2012, The Journal of Neuroscience.
[20] Joel Z. Leibo,et al. Why The Brain Separates Face Recognition From Object Recognition , 2011, NIPS.
[21] Bartlett W. Mel. SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.
[22] D. Marr,et al. Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[23] Talma Hendler,et al. Eccentricity Bias as an Organizing Principle for Human High-Order Object Areas , 2002, Neuron.
[24] Joseph L. Zinnes,et al. Theory and Methods of Scaling. , 1958 .
[25] Angela J. Yu,et al. Uncertainty, Neuromodulation, and Attention , 2005, Neuron.
[26] 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..
[27] Michael W. Spratling. Learning viewpoint invariant perceptual representations from cluttered images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] H. B. Barlow,et al. Possible Principles Underlying the Transformations of Sensory Messages , 2012 .
[29] N. Logothetis,et al. Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.
[30] Dwight J. Kravitz,et al. The ventral visual pathway: an expanded neural framework for the processing of object quality , 2013, Trends in Cognitive Sciences.
[31] Joel Z. Leibo,et al. Learning Generic Invariances in Object Recognition: Translation and Scale , 2010 .
[32] Bevil R. Conway,et al. Parallel, multi-stage processing of colors, faces and shapes in macaque inferior temporal cortex , 2013, Nature Neuroscience.
[33] Garrison W. Cottrell,et al. Organization of face and object recognition in modular neural network models , 1999, Neural Networks.
[34] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[35] Philippe G Schyns,et al. Diagnostic recognition: task constraints, object information, and their interactions , 1998, Cognition.
[36] M. Tarr,et al. Becoming a “Greeble” Expert: Exploring Mechanisms for Face Recognition , 1997, Vision Research.
[37] D. Schacter,et al. On the nature of medial temporal lobe contributions to the constructive simulation of future events , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.
[38] Joel Z. Leibo,et al. The invariance hypothesis and the ventral stream , 2014 .
[39] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[40] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[41] J. Fodor,et al. The Modularity of Mind: An Essay on Faculty Psychology , 1984 .
[42] N. Logothetis,et al. View-dependent object recognition by monkeys , 1994, Current Biology.
[43] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[44] S. Dehaene,et al. Cultural Recycling of Cortical Maps , 2007, Neuron.
[45] M. Tarr,et al. Activation of the middle fusiform 'face area' increases with expertise in recognizing novel objects , 1999, Nature Neuroscience.
[46] M. Livingstone,et al. Behavioral and Anatomical Consequences of Early versus Late Symbol Training in Macaques , 2012, Neuron.
[47] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[48] S. Ullman. Aligning pictorial descriptions: An approach to object recognition , 1989, Cognition.
[49] Santiago Ramón y Cajal,et al. Texture of the Nervous System of Man and the Vertebrates , 1999, Springer Vienna.
[50] David D. Cox,et al. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation , 2009, PLoS Comput. Biol..
[51] Irving Biederman,et al. One-shot viewpoint invariance in matching novel objects , 1999, Vision Research.
[52] Tomaso A. Poggio,et al. Neural tuning size is a key factor underlying holistic face processing , 2014, ArXiv.
[53] Jack L. Gallant,et al. A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain , 2012, Neuron.
[54] K. Grill-Spector,et al. Electrical Stimulation of Human Fusiform Face-Selective Regions Distorts Face Perception , 2012, The Journal of Neuroscience.
[55] 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.
[56] Lorenzo Rosasco,et al. The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). , 2012 .
[57] Talma Hendler,et al. Center–periphery organization of human object areas , 2001, Nature Neuroscience.
[58] Joel Z. Leibo,et al. Learning invariant representations and applications to face verification , 2013, NIPS.
[59] Heinrich H Bülthoff,et al. Image-based object recognition in man, monkey and machine , 1998, Cognition.
[60] Leslie G. Ungerleider,et al. Distributed representation of objects in the human ventral visual pathway. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[61] Rob Fergus,et al. Visualizing and Understanding Convolutional Neural Networks , 2013 .
[62] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[63] E. Marder. Neuromodulation of Neuronal Circuits: Back to the Future , 2012, Neuron.
[64] M. Bar,et al. Scenes Unseen: The Parahippocampal Cortex Intrinsically Subserves Contextual Associations, Not Scenes or Places Per Se , 2008, The Journal of Neuroscience.
[65] Thomas Serre,et al. Component-based face detection , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[66] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[67] G. Mitchison. Neuronal branching patterns and the economy of cortical wiring , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[68] M. Farah,et al. Parts and Wholes in Face Recognition , 1993, The Quarterly journal of experimental psychology. A, Human experimental psychology.
[69] Scott D. Slotnick,et al. The Visual Word Form Area , 2013 .
[70] Stefano Soatto,et al. On the set of images modulo viewpoint and contrast changes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[71] Joel Z. Leibo,et al. Does invariant recognition predict tuning of neurons in sensory cortex ? , 2013 .
[72] S Lehéricy,et al. The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. , 2000, Brain : a journal of neurology.
[73] Laurie S. Glezer,et al. Evidence for Highly Selective Neuronal Tuning to Whole Words in the “Visual Word Form Area” , 2009, Neuron.
[74] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[75] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.
[76] Zhenghao Chen,et al. On Random Weights and Unsupervised Feature Learning , 2011, ICML.
[77] Joel Z. Leibo,et al. Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex , 2017 .
[78] Tomaso Poggio,et al. A hierarchical model of peripheral vision , 2011 .
[79] M J Tarr,et al. Is human object recognition better described by geon structural descriptions or by multiple views? Comment on Biederman and Gerhardstein (1993). , 1995, Journal of experimental psychology. Human perception and performance.
[80] N. Kanwisher. Domain specificity in face perception , 2000, Nature Neuroscience.
[81] N. Kanwisher,et al. The fusiform face area: a cortical region specialized for the perception of faces , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.
[82] Joel Z. Leibo,et al. Unsupervised learning of clutter-resistant visual representations from natural videos , 2014, ArXiv.
[83] Tomaso Poggio,et al. Faces as a "Model Category" for Visual Object Recognition , 2013 .
[84] T Poggio,et al. View-based models of 3D object recognition: invariance to imaging transformations. , 1995, Cerebral cortex.
[85] Lorenzo Rosasco,et al. Word-level invariant representations from acoustic waveforms , 2014, INTERSPEECH.
[86] R. Malach,et al. Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[87] Bradford Z. Mahon,et al. What drives the organization of object knowledge in the brain? , 2011, Trends in Cognitive Sciences.
[88] I. Biederman. Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.
[89] Doris Y. Tsao,et al. Faces and objects in macaque cerebral cortex , 2003, Nature Neuroscience.
[90] T. Poggio,et al. A network that learns to recognize three-dimensional objects , 1990, Nature.
[91] Thomas Serre,et al. A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.
[92] Shimon Ullman,et al. Visual Classification by a Hierarchy of Extended Fragments , 2006, Toward Category-Level Object Recognition.
[93] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[94] Edmund T. Rolls,et al. Invariant Object Recognition in the Visual System with Novel Views of 3D Objects , 2002, Neural Computation.
[95] N. Kanwisher,et al. The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.
[96] Doris Y. Tsao,et al. Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System , 2010, Science.
[97] Lorenzo Rosasco,et al. Learning An Invariant Speech Representation , 2014, ArXiv.
[98] N. Kanwisher,et al. The Human Body , 2001 .
[99] David G. Lowe,et al. Multiclass Object Recognition with Sparse, Localized Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[100] R. Malach,et al. The topography of high-order human object areas , 2002, Trends in Cognitive Sciences.
[101] Joel Z. Leibo,et al. The dynamics of invariant object recognition in the human visual system. , 2014, Journal of neurophysiology.
[102] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[103] D. Plaut,et al. Complementary neural representations for faces and words: A computational exploration , 2011, Cognitive neuropsychology.
[104] Cordelia Schmid,et al. Toward Category-Level Object Recognition , 2006, Toward Category-Level Object Recognition.
[105] Michael J. Tarr. Is human object recognition better described by geon structural description or by multiple views , 1995 .
[106] Doris Y. Tsao,et al. A Cortical Region Consisting Entirely of Face-Selective Cells , 2006, Science.
[107] A. Young,et al. Configurational Information in Face Perception , 1987, Perception.
[108] Guy Wallis,et al. Toward a unified model of face and object recognition in the human visual system , 2013, Front. Psychol..
[109] Doris Y. Tsao,et al. Patches with Links: A Unified System for Processing Faces in the Macaque Temporal Lobe , 2008, Science.
[110] Denis Fize,et al. Speed of processing in the human visual system , 1996, Nature.
[111] T Yamamoto,et al. Selective impairment of facial recognition due to a haematoma restricted to the right fusiform and lateral occipital region , 2001, Journal of neurology, neurosurgery, and psychiatry.
[112] D. Chklovskii,et al. Maps in the brain: what can we learn from them? , 2004, Annual review of neuroscience.
[113] Sameer A. Nene,et al. Columbia Object Image Library (COIL100) , 1996 .
[114] N. Kanwisher. Functional specificity in the human brain: A window into the functional architecture of the mind , 2010, Proceedings of the National Academy of Sciences.
[115] Nancy Kanwisher,et al. A cortical representation of the local visual environment , 1998, Nature.
[116] S. Cajal. Texture of the nervous system of man and the vertebrates , 2000 .
[117] M. Tarr,et al. FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise , 2000, Nature Neuroscience.
[118] Tomaso A. Poggio,et al. Computational role of eccentricity dependent cortical magnification , 2014, ArXiv.
[119] H. Bülthoff,et al. Face recognition under varying poses: The role of texture and shape , 1996, Vision Research.
[120] Isabel Gauthier,et al. What constrains the organization of the ventral temporal cortex? , 2000, Trends in Cognitive Sciences.
[121] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[122] J. Fodor. The Modularity of mind. An essay on faculty psychology , 1986 .
[123] Joel Z. Leibo,et al. Learning and disrupting invariance in visual recognition with a temporal association rule , 2011, Front. Comput. Neurosci..
[124] Edmund T. Rolls,et al. Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet , 2012, Front. Comput. Neurosci..
[125] P. Schyns,et al. Information and viewpoint dependence in face recognition , 1997, Cognition.
[126] Lorenzo Rosasco,et al. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? , 2014 .
[127] Tomaso Poggio,et al. Role of learning in three-dimensional form perception , 1996, Nature.
[128] H. Barlow. Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .
[129] N. Kanwisher,et al. Visual word processing and experiential origins of functional selectivity in human extrastriate cortex , 2007, Proceedings of the National Academy of Sciences.