I build nice little robots at Tesla AI. If you would like to do the same, send me an email!

Before that, I did some postdoctoral studies at Sorbonne and Brown University.

My scientific interest lies in understanding the underlying mechanisms of intelligence. My research is focused on learning human behaviors with neural networks. I am working on novel architectures, learning approaches, theoritical frameworks and explainability methods. I like to contribute to open-source projects and to read about neuroscience!

CV Google Scholar

Publications

A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation

T. Fel, T. V. Boutin, M. Moayeri, R. Cadene, L. Bethune, M. Chalvidal, T. Serre

Arxiv (2023)

Arxiv

@inproceedings{fel2023holistic,
author = {Thomas Fel and Victor Boutin and Mazda Moayeri and Remi Cadene and Louis Bethune and Mathieu Chalvidal and Thomas Serre},
title = {A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation},
booktitle = {Arxiv},
year = {2023},
url = {https://arxiv.org/abs/2306.07304}
}

Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization

T. Fel, T. Boissin, V. Boutin, A. Picard, P. Novello, J. Colin, D. Linsley, T. Rousseau, R. Cadene, L. Gardes, T. Serre

Arxiv (2023)

Arxiv

@inproceedings{fel2023unlocking,
author = {Thomas Fel and Thibaut Boissin and Victor Boutin and Agustin Picard and Paul Novello and Julien Colin and Drew Linsley and Tom Rousseau and Remi Cadene and Laurent Gardes and Thomas Serre},
title = {Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization},
booktitle = {Arxiv},
year = {2023},
url = {https://arxiv.org/abs/2306.06805}
}

CRAFT: Concept recursive activation factorization for explainability

T. Fel, A. Picard, L. Bethune, T. Boissin, D. Vigouroux, J. Colin, R. Cadene, T. Serre

CVPR (2023)

Arxiv

@inproceedings{fel2023craft,
author = {Thomas Fel and Agustin Picard and Louis Bethune and Thibaut Boissin and David Vigouroux and Julien Colin and Rémi Cadène and Thomas Serre},
title = {CRAFT: {C}oncept recursive activation factorization for explainability},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition {CVPR}},
year = {2023},
url = {https://openaccess.thecvf.com/content/CVPR2023/papers/Fel_CRAFT_Concept_Recursive_Activation_FacTorization_for_Explainability_CVPR_2023_paper.pdf}
}

Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis

T. Fel, M. Ducoffe, D. Vigouroux, R. Cadène, M. Capelle, C. Nicodeme, T. Serre

CVPR (2023)

Arxiv

@inproceedings{fel2023dontlieeva,
author = {Thomas Fel and Melanie Ducoffe and David Vigouroux and Remi Cadene and Mikael Capelle and Claire Nicodeme and Thomas Serre},
title = {Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition {CVPR}},
year = {2023},
url = {https://openaccess.thecvf.com/content/CVPR2023/papers/Fel_Dont_Lie_to_Me_Robust_and_Efficient_Explainability_With_Verified_CVPR_2023_paper.pdf}
}

What I cannot predict, I do not understand: A human-centered evaluation framework for explainability methods

J. Colin, T. Fel, R. Cadene, T. Serre

NeurIPS (2022)

Arxiv

@inproceedings{fel2022whaticannotpredict,
author = {Julien Colin andThomas Fel andRemi Cadene andThomas Serre},
title = {What {I} cannot predict, {I} do not understand: {A} human-centered evaluation framework for explainability methods},
booktitle = {Advances in Neural Information Processing Systems 35},
year = {2022},
url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/13113e938f2957891c0c5e8df811dd01-Paper-Conference.pdf}
}

Xplique: A deep learning explainability toolbox

T. Fel, L. Hervier, D. Vigouroux, A. Poche, J. Plakoo, R. Cadene, M. Chalvidal, J. Colin, T. Boissin, L. Bethune, A. Picard, C. Nicodeme, L. Gardes, G. Flandin, T. Serre

Arxiv (2022)

Arxiv

@inproceedings{fel2022xplique,
author = {Thomas Fel and Lucas Hervier and David Vigouroux and Antonin Poche and Justin Plakoo and Remi Cadene and Mathieu Chalvidal and Julien Colin and Thibaut Boissin and Louis Bethune and Agustin Picard and Claire Nicodeme and Laurent Gardes and Gregory Flandin and Thomas Serre},
title = {Xplique: A deep learning explainability toolbox},
booktitle = {Arxiv},
year = {2022},
url = {https://arxiv.org/abs/2206.04394}
}

How good is your explanation? algorithmic stability measures to assess the quality of explanations for deep neural networks

T. Fel, D. Vigouroux, R. Cadene, T. Serre

WACV (2022)

Arxiv

@inproceedings{fel2022howgood,
author = {Thomas Fel and David Vigouroux and Remi Cadene and Thomas Serre},
title = {How good is your explanation? algorithmic stability measures to assess the quality of explanations for deep neural networks},
booktitle = {{IEEE} Winter Conference on Applications of Computer Vision {WACV}},
year = {2022},
url = {https://openaccess.thecvf.com/content/WACV2022/papers/Fel_How_Good_Is_Your_Explanation_Algorithmic_Stability_Measures_To_Assess_WACV_2022_paper.pdf}
}

Understanding the computational demands underlying visual reasoning

M. Vaishnav, R. Cadene, A. Alamia, D. Linsley, R. VanRullen, T. Serre

MIT Press (2022)

Paper

@article{vaishnav2022understanding,
author = {Mohit Vaishnav and Remi Cadene and Andrea Alamia and Drew Linsley and Rufin VanRullen and Thomas Serre}
year = {2022},
title = {Understanding the computational demands underlying visual reasoning},
journal = {Neural Computation},
volume = {32},
issue = {5},
pages = {1075 - 1099},
doi = {https://doi.org/10.1162/neco_a_01485},
url = {https://direct.mit.edu/neco/article/34/5/1075/109662/Understanding-the-Computational-Demands-Underlying}
}

Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis

T. Fel*, R. Cadene*, M. Chalvidal, M. Cord, D. Vigouroux, T. Serre

NeurIPS (2021)

Arxiv

@inproceedings{fel2021sobol,
author = {Thomas Fel and Remi Cadene and Mathieu Chalvidal and Matthieu Cord and David Vigouroux and Thomas Serre},
title = {Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis},
booktitle = {Advances in Neural Information Processing Systems 34},
year = {2021},
url = {https://proceedings.neurips.cc/paper/2021/file/da94cbeff56cfda50785df477941308b-Paper.pdf}
}

Deep Multimodal Learning for Vision and Language Processing

R. Cadene

Sorbonne Université (2021)

Thesis

@phdthesis{cadene2021multimodal,
author={R. Cadene},
year = {2021},
title = {Deep Multimodal Learning for Vision and Language Processing},
school={Sorbonne Universit{\'e}, UPMC},
url = {https://hal.archives-ouvertes.fr/tel-03140942}
}

Same-different conceptualization: a machine vision perspective

M. Ricci, R. Cadene, T. Serre

Elsevier COBS (2021)

Paper

@article{ricci2020samedifferent,
author = {Matthew Ricci and Rémi Cadène and Thomas Serre}
year = {2021},
title = {Same-different conceptualization: a machine vision perspective},
journal = {Current Opinion in Behavioral Sciences},
volume = {37},
pages = {47 - 55},
issn = {2352-1546},
doi = {https://doi.org/10.1016/j.cobeha.2020.08.008},
url = {http://www.sciencedirect.com/science/article/pii/S2352154620301352}
}

Overcoming Statistical Shortcuts for Open-ended Visual Counting

C. Dancette*, R. Cadene*, X. Chen, M. Cord

Arxiv (2020)

Arxiv

@inproceedings{dancette2020counting,
author = {Dancette, Corentin and Cadene, Remi and Chen, Xinlei and Cord, Matthieu},
title = {Overcoming Statistical Shortcuts for Open-ended Visual Counting},
booktitle = {Arxiv},
year = {2020},
url = {https://arxiv.org/abs/2006.10079}
}

RUBi: Reducing Unimodal Biases for Visual Question Answering

R. Cadene*, C. Dancette*, H. Ben-Younes, M. Cord, D. Parikh

NeurIPS (2019)

Arxiv Code

@inproceedings{cadene2019rubi,
author = {Cadene, Remi and Dancette, Corentin and Ben-Younes, Hedi and Cord, Matthieu and Parikh, Devi},
title = {{RUB}i: {R}educing {U}nimodal {B}iases for {V}isual {Q}uestion {A}nswering},
booktitle = {Advances in Neural Information Processing Systems 32},
year = {2019},
url = {https://arxiv.org/abs/1906.10169}
}

MUREL: Multimodal Relational Reasoning for Visual Question Answering

R. Cadene*, H. Ben-Younes*, N. Thome, M. Cord

CVPR (2019)

Arxiv Code

@inproceedings{cadene2019murel,
author = {Cadene, Remi and Ben-Younes, Hedi and Thome, Nicolas and Cord, Matthieu},
title = {MUREL: {M}ultimodal {R}elational {R}easoning for {V}isual {Q}uestion {A}nswering},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition {CVPR}},
year = {2019},
url = {https://arxiv.org/abs/1902.09487}
}

BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection

H. Ben-Younes, R. Cadene, N. Thome, M. Cord

AAAI (2019)

Arxiv Poster Code

@inproceedings{benyounes2019block,
author = {Ben-Younes, Hedi and Cadene, Remi and Thome, Nicolas and Cord, Matthieu},
title = {BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection},
booktitle = {Proceedings of the 33st Conference on Artificial Intelligence (AAAI)},
year = {2019},
url = {https://arxiv.org/abs/1902.00038}
}

Bootstrap.pytorch, a high-level framework for accelerating research

R. Cadene, M. Carvalho, H. Ben-Younes, T. Robert, M. Cord

Pytorch (2018)

Poster Code

Benchmark Analysis of Representative Deep Neural Network Architectures

S. Bianco, R. Cadene, L. Celona, P. Napoletano

IEEE Access (2018)

Arxiv Code

@article{bianco2018dnnsbench,
author = {Bianco, Simone and Cadene, Remi and Celona, Luigi and Napoletano, Paolo},
year = {2018},
title = {Benchmark Analysis of Representative Deep Neural Network Architectures},
journal = {IEEE Access},
volume = {6},
pages = {64270-64277},
doi = {10.1109/ACCESS.2018.2877890},
ISSN = {2169-3536},
}

VQA Challenge Workshop: Bilinear Superdiagonal Fusion

H. Ben-Younes, R. Cadene, N. Thome, M. Cord

VQA Workshop (CVPR) (2018)

Code

Cross-modal retrieval in the cooking context: Learning semantic text-image embeddings

M. Carvalho*, R. Cadene* D. Picard, L. Soulier, N. Thome, M. Cord

SIGIR (2018)

Arxiv Code

@inproceedings{carvalho2018adamine,
author = {Carvalho, Micael and Cadene, Remi and Picard, David and Soulier, Laure and Thome, Nicolas and Cord, Matthieu},
title = {Cross-modal retrieval in the cooking context: {L}earning semantic text-image embeddings},
booktitle = {The ACM Conference on Research and Development in Information Retrieval (SIGIR)},
year = {2018},
url = {https://arxiv.org/abs/1804.11146}
}

Images & Recipes: Retrieval in the cooking context

M. Carvalho*, R. Cadene*, D. Picard, L. Soulier, M. Cord

DECOR Workshop (ICDE) (2018)

Arxiv Slides Code

MUTAN: Multimodal Tucker Fusion for Visual Question Answering

H. Ben-Younes*, R. Cadene*, N. Thome, M. Cord

ICCV (2017)

Arxiv Slides Code

@inproceedings{benyounes2017mutan,
author = {Ben-younes, Hedi and Cadene, Remi and Cord, Matthieu and Thome, Nicolas},
title = {{MUTAN}: {M}ultimodal {T}ucker {F}usion for {V}isual {Q}uestion {A}nswering},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017},
url = {http://arxiv.org/abs/1705.06676}
}

VQA Challenge Workshop: MUTAN 2.0

H. Ben-Younes*, R. Cadene*, N. Thome, M. Cord

VQA Workshop (CVPR) (2017)

Poster Code

Master's Thesis - Deep Learning for Visual Recognition

R. Cadene, N. Thome, M. Cord

(2016)

Arxiv Slides Code

@article{DBLP:journals/corr/CadeneTC16,
author = {R{\'{e}}mi Cad{\`{e}}ne and
Nicolas Thome and
Matthieu Cord},
title = {Master's Thesis : Deep Learning for Visual Recognition},
journal = {CoRR},
volume = {abs/1610.05567},
year = {2016},
url = {http://arxiv.org/abs/1610.05567},
timestamp = {Wed, 02 Nov 2016 09:51:26 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/CadeneTC16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}

M2CAI Workflow Challenge: Convolutional Neural Networks for Video Frames Classification

R. Cadene, T. Robert, N. Thome, M. Cord

M2CAI Workshop (MICCAI) (2016)

Arxiv Poster Code

@article{DBLP:journals/corr/CadeneRTC16,
author = {R{\'{e}}mi Cad{\`{e}}ne and
Thomas Robert and
Nicolas Thome and
Matthieu Cord},
title = {{M2CAI} Workflow Challenge: Convolutional Neural Networks with Time
Smoothing and Hidden Markov Model for Video Frames Classification},
journal = {CoRR},
volume = {abs/1610.05541},
year = {2016},
url = {http://arxiv.org/abs/1610.05541},
timestamp = {Wed, 02 Nov 2016 09:51:26 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/CadeneRTC16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
* equal contribution

Contact

Couloir 26-00, Bureau 523
4, place Jussieu
75005 Paris
France
@remicadene
Cadene