A Brown University Research Group

    Work in progress

  1. Early ventral stream neural activity enables rapid categorization

    In submission

    M. Cauchoix*, S. Crouzet*, D. Fize & T. Serre

  2. Models of visual categorization

    In revision, WIREs Cognitive Science

    T. Serre

    2014

  1. The language of actions: Recovering the syntax and semantics of goal-directed human activities

    IEEE Conference on Computer Vision and Pattern Recognition

    H. Kuehne, A. Arlsan & T. Serre

  2. Hierarchical models of the visual system

    Encyclopedia of Computational Neuroscience

    T. Serre

  3. Neuronal synchrony in complex-valued deep networks

    International Conference on Learning Representations

    D. Reichert & T. Serre

  4. The neural dynamics of face detection in the wild revealed by MVPA

    Journal of Neuroscience

    M. Cauchoix^, G. Barragan-­Jason^, T. Serre* & E.J. Barbeau* (^,* are authors with equal contributions)

    2013

  1. Neural representation of action sequences: How far can a simple snippet-matching model take us?

    Neural Information Processing Systems

    C. Tan, J. Singer, T. Serre, D. Sheinberg & T. Poggio

  2. Models of the visual cortex

    Scholarpedia, 8(4):3516.

    T. Poggio & T. Serre

    2012

  1. The ankyrin 3 (ANK3) bipolar disorder gene regulates mood-related behaviors that are modulated by lithium and stress*

    Biological Psychiatry

    M. Leussis, E. Berry-Scott, M. Saito, H. Jhuang, G. Haan, O. Alkan, C. Luce, J. Madison, P. Sklar, T. Serre, D. Root, T. Petryshen

  2. A new biologically inspired color image descriptor

    Proceedings of the European Computer Vision Conference

    J. Zhang, Y. Barhomi, T. Serre

  3. The neural dynamics of visual processing in monkey extrastriate cortex: A comparison between univariate and multivariate techniques

    Neural Information Processing Systems – Workshop on Machine Learning and Interpretation in Neuroimaging

    M. Cauchoix, A. Arslan, D. Fize, T. Serre

    2011

  1. What are the visual features underlying rapid recognition?

    Frontiers in Psychology

    S.M. Crouzet & T. Serre

  2. Object decoding with attention in inferior temporal cortex

    Proceedings of the National Academy of Sciences

    Y. Zhang*, E. Meyers*, N. Bichot, T. Serre, T. Poggio, R. Desimone

  3. HMDB: A large video database for human motion recognition

    IEEE International Computer Vision Conference

    H. Kuhne, H. Jhuang, E. Garrote, T. Poggio, T. Serre

    2010

  1. Automated home-cage behavioral phenotyping of mice

    Nature Communications

    H. Jhuang, E. Garrote, X. Yu, V. Khilnani, T. Poggio, A. Steele, T. Serre

  2. What and where: A Bayesian inference theory of attention

    Vision Research

    S. Chikkerur, T. Serre, C. Tan, T. Poggio

  3. Elements for a neural theory of the processing of dynamic faces

    Dynamic Faces: Insights from Experiments and Computation

    T. Serre, M. Giese

  4. Reading the mind’s eye: Decoding category information during mental imagery

    NeuroImage

    L. Reddy, N. Tsuchyia, T. Serre

  5. The story of a single cell: Peeking into the semantics of spikes

    IAPR Workshop on Cognitive Information Processing

    R. Kliper, T. Serre, D. Weinshall, I. Nelken

  6. A neuromorphic approach to computer vision

    Communications of the ACM

    T. Serre & T. Poggio

    2009

  1. Attentive processing improves object recognition

    MIT Computer Science and Artificial Intelligence Laboratory Technical Report

    S. Chikkerur, T. Serre, T. Poggio

  2. A Bayesian inference theory of attention: neuroscience and algorithms

    MIT Computer Science and Artificial Intelligence Laboratory Technical Report

    S. Chikkerur, T. Serre, T. Poggio

    2007

  1. Robust object recognition with cortex-like mechanisms

    IEEE Transactions on Pattern Analysis and Machine Intelligence

    T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio

  2. A feedforward architecture accounts for rapid categorization*

    Proceedings of the National Academy of Science

    T. Serre, A. Oliva, T. Poggio

  3. A quantitative theory of immediate visual recognition

    Progress in Brain Research, Computational Neuroscience: Theoretical Insights into Brain Function

    T. Serre, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich, T. Poggio

  4. Learning complex cell invariance from natural videos: a plausibility proof

    MIT Computer Science and Artificial Intelligence Laboratory Technical Report

    T. Masquelier, T. Serre, S. Thorpe, T. Poggio

  5. A biologically inspired system for action recognition

    Proceedings of the Eleventh IEEE International Conference on Computer Vision

    H. Jhuang, T. Serre, L. Wolf, T. Poggio

  6. A component-based framework for face detection and identification

    International Journal of Computer Vision

    B. Heisele, T. Serre, T. Poggio

    2006

  1. Learning a dictionary of shape-components in visual cortex: Comparison with neurons, humans and machines

    MIT Computer Science and Artificial Intelligence Laboratory Technical Report

    T. Serre

    2005

  1. A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex

    MIT Computer Science and Artificial Intelligence Laboratory

    T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, T. Poggio

  2. Learning features of intermediate complexity for the recognition of biological motion

    ICANN 2005

    R. Sigala, T. Serre, T. Poggio, M. Giese

  3. Object recognition with features inspired by visual cortex

    IEEE Computer Vision and Pattern Recognition Conference

    T. Serre, L. Wolf, T. Poggio

  4. Error weighted classifier combination for multi-modal human identification

    MIT Computer Science and Artificial Intelligence Laboratory Technical Report

    Y. Ivanov, T. Serre, J. Bouvrie

    2004

  1. Realistic modeling of simple and complex cell tuning in the HMAX model, and implications for invariant object recognition in cortex

    MIT Computer Science and Artificial Intelligence Laboratory

    T. Serre, M. Riesenhuber

  2. A new biologically motivated framework for robust object recognition

    MIT Computer Science and Artificial Intelligence Laboratory

    T. Serre, L. Wolf, T. Poggio

  3. Using component features for face recognition

    International Conference on Automatic Face and Gesture Recognition

    Y. Ivanov, B. Heisele, T. Serre

    2003

  1. Hierarchical classification and feature reduction for fast face detection with support vector machines

    Pattern Recognition

    B. Heisele, T. Serre, S. Prentice, T. Poggio

    2002

  1. On the role of object-specific features for real-world object recognition in biological vision

    Workshop on Biologically Motivated Computer Vision

    T. Serre, J. Louie, M. Riesenhuber, T. Poggio

  2. Categorization by learning and combining object parts

    Advances in Neural Information Processing Systems

    B. Heisele, T. Serre, M. Pontil, T. Vetter, T. Poggio

    2001

  1. Feature reduction and hierarchy of classifiers for fast object detection in video images

    Computer Vision and Pattern Recognition

    B. Heisele, T. Serre, S. Mukherjee, T. Poggio

  2. Component-based face detection

    Computer Vision and Pattern Recognition

    B. Heisele, T. Serre, M. Pontil, T. Poggio

    2000

  1. Feature selection for face detection

    MIT Center for Biological and Computational Learning

    T. Serre, B. Heisele, S. Mukherjee, T. Poggio

    Year
  1. Work in progress
  2. 2014
  3. 2013
  4. 2012
  5. 2011
  6. 2010
  7. 2009
  8. 2007
  9. 2006
  10. 2005
  11. 2004
  12. 2003
  13. 2002
  14. 2001
  15. 2000
  16. Google Scholar Citations