Thomas Serre

Sayan Mukherjee, Thomas Serre, Tomaso A. Poggio, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Trainable, vision-based automated home cage behavioral phenotyping

Andrew Steele, Thomas Serre, Tomaso A. Poggio, 2010, MB '10.

Stable and expressive recurrent vision models pdf

Thomas Serre, Drew Linsley, Lakshmi Narasimhan Govindarajan, 2020, NeurIPS.

How Deep is the Feature Analysis underlying Rapid Visual Categorization?

Thomas Serre, Sven Eberhardt, Jonah Cader, 2016, NIPS.

Same-different conceptualization: a machine vision perspective

Thomas Serre, Rémi Cadène, Matthew Ricci, 2021, Current Opinion in Behavioral Sciences.

On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision

Thomas Serre, Tomaso A. Poggio, Maximilian Riesenhuber, 2002, Biologically Motivated Computer Vision.

Object decoding with attention in inferior temporal cortex

Thomas Serre, Ying Zhang, Tomaso A Poggio, 2011, Proceedings of the National Academy of Sciences.

An end-to-end generative framework for video segmentation and recognition

Thomas Serre, Juergen Gall, Hildegard Kuehne, 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

Daphna Weinshall, Thomas Serre, Israel Nelken, 2010, 2010 2nd International Workshop on Cognitive Information Processing.

How does the visual system create complex shape and motion features

Thomas Serre, Tomaso Poggio, Cheston Tan, 2011 .

Using component features for face recognition

Thomas Serre, Yuri Ivanov, Bernd Heisele, 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

Large-scale discovery of visual features for object recognition

Thomas Serre, Drew Linsley, Sven Eberhardt, 2017 .

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

Thomas Serre, Tomaso A. Poggio, Cheston Tan, 2013, NIPS.

Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks pdf

Thomas Serre, Junkyung Kim, Matthew Ricci, 2018, ICLR 2018.

Feature Selection for Face Detection

Thomas Serre, Tomaso Poggio, Sayan Mukherjee, 2000 .

Cortically-inspired inhibition subtends better contour integration

Thomas Serre, David A. Mély, Thomas Serre, 2013 .

A quantitative theory of immediate visual recognition.

Thomas Serre, Gabriel Kreiman, Tomaso Poggio, 2007, Progress in brain research.

Learning Sparse Prototypes for Crowd Perception via Ensemble Coding Mechanisms

Qingming Huang, Shengping Zhang, Thomas Serre, 2014, HBU.

Categorization by Learning and Combining Object Parts

Thomas Serre, Tomaso A. Poggio, Massimiliano Pontil, 2001, NIPS.

Unsupervised invariance learning of transformation sequences in a model of object recognition yields selectivity for non-accidental properties

Thomas Serre, Sarah M. Parker, Thomas Serre, 2015, Front. Comput. Neurosci..

Learning to predict action potentials end-to-end from calcium imaging data

Thomas Serre, Drew Linsley, Tarun Sharma, 2018, 2018 52nd Annual Conference on Information Sciences and Systems (CISS).

Fast ventral stream neural activity enables rapid visual categorization

Thomas Serre, Denis Fize, Maxime Cauchoix, 2015, NeuroImage.

Elements for a Neural Theory of the Processing of Dynamic Faces

Martin A. Giese, Thomas Serre, M. Giese, 2010 .

Recurrent neural circuits for contour detection pdf

Thomas Serre, Drew Linsley, Junkyung Kim, 2020, ICLR.

A systematic comparison between visual cues for boundary detection

Thomas Serre, Mason McGill, Yuliang Guo, 2016, Vision Research.

A neuromorphic approach to computer vision

Thomas Serre, Tomaso A. Poggio, T. Poggio, 2010, Commun. ACM.

Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks pdf

Thomas Serre, Drew Linsley, Junkyung Kim, 2018, ArXiv.

Opponent surrounds explain diversity of contextual phenomena across visual modalities

Thomas Serre, David A. Mély, Thomas Serre, 2016, bioRxiv.

Same-different problems strain convolutional neural networks

Thomas Serre, Junkyung Kim, Matthew Ricci, 2018, CogSci.

What are the Visual Features Underlying Human Versus Machine Vision?

Thomas Serre, P. Gupta, Drew Linsley, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

A novel game for discovering visual features for object recognition.

Thomas Serre, Pankaj Gupta, Drew Linsley, 2017 .

Learning what and where to attend

Thomas Serre, Drew Linsley, Sven Eberhardt, 2018, ICLR.

Towards a Unified Model of Classical and Extra-Classical Receptive Fields

Thomas Serre, David A. Mély, Thomas Serre, 2016 .

Global-and-local attention networks for visual recognition pdf

Thomas Serre, Drew Linsley, Sven Eberhardt, 2018, ArXiv.

Object recognition with features inspired by visual cortex

Thomas Serre, Lior Wolf, Tomaso A. Poggio, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

Modeling feature sharing between object detection and top-down attention

Thomas Serre, Tomaso Poggio, Christof Koch, 2005 .

Component-based face detection

Thomas Serre, Tomaso A. Poggio, Massimiliano Pontil, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Towards a generative approach to activity recognition and segmentation pdf

Thomas Serre, Hildegard Kuehne, Thomas Serre, 2015, ArXiv.

Hierarchical Models of the Visual System

Thomas Serre, Thomas Serre, 2014, Encyclopedia of Computational Neuroscience.

Computer vision cracks the leaf code

Shengping Zhang, Thomas Serre, Sharat Chikkerur, 2016, Proceedings of the National Academy of Sciences.

Learning complex cell invariance from natural videos: A plausibility proof

Thomas Serre, Tomaso Poggio, Timothée Masquelier, 2007 .

Models of visual categorization.

Thomas Serre, Thomas Serre, 2016, Wiley interdisciplinary reviews. Cognitive science.

Neuronal Synchrony in Complex-Valued Deep Networks pdf

Thomas Serre, David P. Reichert, Thomas Serre, 2013, ICLR.

Models of visual cortex

Thomas Serre, Tomaso A. Poggio, T. Poggio, 2013, Scholarpedia.

Towards a Theory of Computation in the Visual Cortex

Thomas Serre, David A. Mély, Thomas Serre, 2017 .

Neural Optimal Control for Representation Learning pdf

Thomas Serre, Rufin VanRullen, Matthew Ricci, 2020, ArXiv.

Hierarchical Classification and Feature Reduction for Fast Face Detection

Thomas Serre, Tomaso Poggio, Bernd Heisele, 2005 .

Cooking in the kitchen: A generative approach to the recognition, parsing and segmentation of human daily activities pdf

Thomas Serre, Hildegard Kuehne, Thomas Serre, 2015, ArXiv.

Not-So-CLEVR: learning same–different relations strains feedforward neural networks

Thomas Serre, Junkyung Kim, Matthew Ricci, 2018, Interface Focus.

Visual cue diagnosticity for boundary detection in natural scenes: A computational study

Thomas Serre, Mason McGill, Yuliang Guo, 2014 .

Large-scale identification of the visual features used for object recognition with ClickMe.ai

Thomas Serre, Drew Linsley, Sven Eberhardt, 2018, Journal of Vision.

Automated home-cage behavioural phenotyping of mice.

Thomas Serre, Tomaso Poggio, Jim Mutch, 2010, Nature communications.

HMDB: A large video database for human motion recognition

Thomas Serre, Hilde Kuehne, Tomaso Poggio, 2011, 2011 International Conference on Computer Vision.

The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities

Thomas Serre, Ali Bilgin Arslan, Hildegard Kuehne, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

A feedforward architecture accounts for rapid categorization

Thomas Serre, Tomaso Poggio, Aude Oliva, 2007, Proceedings of the National Academy of Sciences.

A data-driven approach to learning strategies for the visual control of navigation.

Stéphane Bonneaud, William H. Warren, Thomas Serre, 2014 .

Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex

Thomas Serre, Maximilian Riesenhuber, M. Riesenhuber, 2004 .

Optimizing a recurrent neural architecture for contour detection produces a tilt illusion

Thomas Serre, Drew Linsley, Junkyung Kim, 2019 .

Go with the Flow: Adaptive Control for Neural ODEs

Thomas Serre, Rufin VanRullen, Matthew Ricci, 2020 .

A New Biologically Inspired Color Image Descriptor

Thomas Serre, Jun Zhang, Youssef Barhomi, 2012, ECCV.

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

Thomas Serre, Tomaso A. Poggio, Bernd Heisele, 2003, Pattern Recognit..

Robust pose tracking with a joint model of appearance and shape pdf

Thomas Serre, Benjamin B. Kimia, Yuliang Guo, 2018, ArXiv.

Iterative VAE as a predictive brain model for out-of-distribution generalization pdf

Thomas Serre, Minju Jung, Aimen Zerroug, 2020, ArXiv.

Reading the mind's eye: Decoding category information during mental imagery

Thomas Serre, Leila Reddy, Naotsugu Tsuchiya, 2010, NeuroImage.

An integrated model of visual attention using shape-based features

Thomas Serre, Sharat Chikkerur, Tomaso Poggio, 2009 .

A canonical circuit for visual contextual integration explains induction effects across visual modalities.

Thomas Serre, David Mély, 2015, Journal of vision.

A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex

Thomas Serre, Tomaso Poggio, Gabriel Kreiman, 2005 .

Disentangling neural mechanisms for perceptual grouping pdf

Thomas Serre, Junkyung Kim, Drew Linsley, 2019, ICLR.

Attention as a Bayesian inference process

Thomas Serre, Tomaso A. Poggio, Sharat Chikkerur, 2011, Electronic Imaging.

Robust Object Recognition with Cortex-Like Mechanisms

Thomas Serre, Lior Wolf, Tomaso A. Poggio, 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Go with the flow: Adaptive control for Neural ODEs

Thomas Serre, Rufin VanRullen, Matthew Ricci, 2020, ICLR.

Deep Learning Networks and Visual Perception

Thomas Serre, Grace W. Lindsay, 2021, Oxford Research Encyclopedia of Psychology.

Understanding the Computational Demands Underlying Visual Reasoning pdf

Thomas Serre, Andrea Alamia, Drew Linsley, 2021, Neural Computation.

KuraNet: Systems of Coupled Oscillators that Learn to Synchronize pdf

Thomas Serre, Minju Jung, Matthew Ricci, 2021, ArXiv.

What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods pdf

Thomas Serre, Thomas Fel, Remi Cadene, 2021, ArXiv.

Tracking Without Re-recognition in Humans and Machines pdf

Thomas Serre, Junkyung Kim, Drew Linsley, 2021, NeurIPS.

Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis pdf

Matthieu Cord, Thomas Serre, Thomas Fel, 2021, NeurIPS.

The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks pdf

Thomas Serre, Drew Linsley, Ennio Mingolla, 2021, ArXiv.

Uncovering the circuit mechanisms that shape contextual phenomena with task-optimized recurrent neural networks

Thomas Serre, Drew Linsley, Lakshmi Govindarajan, 2021, Journal of Vision.

Hierarchical Models of the Visual System

Thomas Serre, Matthew Ricci, Thomas Serre, 2014, Encyclopedia of Computational Neuroscience.

Complementary Surrounds Explain Diverse Contextual Phenomena Across Visual Modalities

Thomas Serre, Drew Linsley, David A. Mély, 2018, Psychological review.

Real-Time Face Detection

Thomas Serre, Bernd Heisele, B. Heisele, 2001 .