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 Big Data - Data Science. Previously I've been intern at Xerox Research Centre Europe.

Email / CV / Github / LinkedIn / Twitter

News

July 2018: One paper accepted to BMVC'18 and one paper accepted to ECCV'18 !
June 2018: New paper on arXiv - "Object level visual reasoning in videos"
February 2018: One paper accepted to CVPR'18 ! More news coming soon.
February 2018: I will spend 4 months at Vision & Media lab in Simon Fraser University under direction of Dr. Greg Mori
August 2017: Two papers accepted to ICCVW'17 (TASK-CV - HANDS)
Summer 2017: I spend the summer at University of Guelph under direction of Dr. Graham Taylor
June 2017: I am participating at the MILA Deep & Reinforcement Learning Summer School in Montreal!
March 2017: New arXiv paper! "Pose-conditioned Spatio-Temporal Attention for Human Action Recognition"
January 2017: Seminar on "Introduction to Deep Learning with Tensorflow" at ENSAI

Research

My main researchs focus on video understanding. I am interested in machine learning, deep learning, computer vision and domain adaptation. I am also interested about causal inference and unsupervised learning in general.

	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 A model capable of learning to reason about semantically meaningful spatio-temporal interactions in videos.
	Human Activity Recognition with Pose-driven Attention to RGB Fabien Baradel, Christian Wolf, Julien Mille The British Machine Vision Conference (BMVC), 2018 PDF / bibtex 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.
	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 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.
	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 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.
	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.
	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.