Fabien Baradel

I am a PhD student in computer vision and machine learning at INSA Lyon under direction of Christian Wolf and Julien Mille. I am also a student researcher intern at Google Research in the Perception team under supervision of Cordelia Schmid. My research interests focus on video understanding and deep learning. I received my Engineer's degree (MSc) from ENSAI.

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News
Publications
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COPHY: Counterfactual Learning of Physical Dynamics
Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, Christian Wolf
ICLR, 2020   (Spotlight presentation)
PDF / arXiv / bibtex

We introduce a new problem of counterfactual learning of object mechanics from visual input and a benchmark called COPHY.

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Learning Video Representations using Contrastive Bidirectional Transformer
Chen Sun, Fabien Baradel, Kevin Murphy, Cordelia Schmid
arXiv preprint, 2019
PDF / arXiv / bibtex

Self-supervised video representation by leveraging long videos via contrastive learning.

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Object Level Visual Reasoning in Videos
Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille, Greg Mori
ECCV, 2018
Project page / PDF / arXiv / video / bibtex / Code / Complementary Mask Data / Poster

A model capable of learning to reason about semantically meaningful spatio-temporal interactions in videos.

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Human Activity Recognition with Pose-driven Attention to RGB
Fabien Baradel, Christian Wolf, Julien Mille
BMVC, 2018
PDF / bibtex / Poster

Human activity recogntion using skeleton data and RGB. We propose a network able to focus on relevant parts of the RGB stream given deep features extracted from the pose stream.

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Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
Fabien Baradel, Christian Wolf, Julien Mille, Graham Taylor
CVPR, 2018
PDF / arXiv / project page / video / bibtex / CVPR Daily / Code / Poster

We propose a new method for human action recognition relying on RGB data only. A visual attention module is able to extract glimpses within each frame. Resulting local descriptors are soft-assigned to distributed workers which are finally classifying the video.

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Human Action Recognition: Pose-based Attention draws focus to Hands
Fabien Baradel, Christian Wolf, Julien Mille
ICCV, Workshop "Hands in Action", 2017
PDF / bibtex / Poster

A new spatio-temporal attention based mechanism for human action recognition able to automatically attend to most important human hands and detect the most discriminative moments in an action.

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Discrepancy-based networks for unsupervised domain adaptation: a comparative study
Gabriela Csurka, Fabien Baradel, Boris Chidlovskii, Stephane Clinchant,
ICCV, Workshop "Task-CV", 2017
PDF / bibtex

We introduce a new dataset for Domain Adaptation and show a comparaison between shallow and deep methods based on Maximum Mean Discrepancy.

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Pose-conditioned Spatio-Temporal Attention for Human Action Recognition
Fabien Baradel, Christian Wolf, Julien Mille
arXiv preprint, 2017
arXiv / PDF / project page / video / bibtex

We introduce an attention-based mechanism around hands on RGB videos conditioned on features extracted from human 3D pose.

Patents
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Conditional adaptation network for image classification
Fabien Baradel, Gabriela Csurka, Boris Chidlovskii,
US Patent App. 15/450,620 - Xerox Corp, 2017
PDF / bibtex

We introduce a new method based on Conditional Maximum Mean Discrepancy for domain adaptation on image classification.

Reviewer ECCV 2020, CVPR 2019-2020, ICML 2019, NIPS 2018, IJCV, TNNLS
Talks
Teaching
teaching

Machine Learning
Science U - M2 Info - 28h30 (CM+TP) - 2018/2019
Github repo / Slides: 1 - 2 / Exercises: 1 - 2

Regression Modelling
Univ Lyon 1 - M2 Data Science - 12h (TP) - Fall 2017

Probability & Statistics
Univ Lyon 1 - L2 Info & Maths-Eco - 12h+8h (TP) - Fall 2017

Mathematics
EPITA - 1st year - 24h (CM+TD) - September 2017

Introduction to Deep Learning with Tensorflow
ENSAI - MSc Data Science - 6h - January 2017
Github repo / slides


Awesome webpage...