Fabien Baradel

I am a third year PhD Candidate at INSA Lyon - LIRIS.
I am working on Machine Learning and Computer Vision.
My supervisors are Christian Wolf and Julien Mille.
My PhD focuses on video understanding.

PhD thesis title:
"Deep Learning for Human Understanding:
poses, gestures, activities"

funded by the ANR/NSREC DeepVision project.

I received my Engineer's degree (MSc) from ENSAI with a major in Data Science. Previously I've been intern at Xerox Research Centre Europe.

Email  /  CV  /  Scholar  /  Github  /  LinkedIn  /  Twitter

News
Research

My main researchs focus on video understanding (e.g. action recognition, human-object interaction). I am also interested into discovering causal concepts from video by leveraging the arrow of time.
Keywords: machine learning, deep learning, computer vision, domain adaptation, causality.

Publications
blind-date

Object Level Visual Reasoning in Videos
Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille Greg Mori
The IEEE European Conference in Computer Vision (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.

blind-date

Human Activity Recognition with Pose-driven Attention to RGB
Fabien Baradel, Christian Wolf, Julien Mille
The British Machine Vision Conference (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.

blind-date

Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
Fabien Baradel, Christian Wolf, Julien Mille Graham Taylor
The IEEE Conference on Computer Vision and Pattern Recognition (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.

blind-date

Human Action Recognition: Pose-based Attention draws focus to Hands
Fabien Baradel, Christian Wolf, Julien Mille
The IEEE International Conference on Computer Vision (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.

blind-date

Discrepancy-based networks for unsupervised domain adaptation: a comparative study
Gabriela Csurka, Fabien Baradel, Boris Chidlovskii, Stephane Clinchant,
The IEEE International Conference on Computer Vision (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.

blind-date

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
blind-date

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 :ICML 2019, CVPR 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...