FU Guanghui

PhD student

Sorbonne University

guanghui.fu [at] icm-institute.org

Short bio

Master of Software Engineering; Beijing University of Technology

Thesis topic

Segmentation, classification and generative models for computer-aided diagnosis of neurological diseases from neuroimaging data.

Short abstract

The objective of this project is to design and validate deep learning methods for computer-assisted diagnosis of neurological disorders, and in particular methods that can deal with applications where annotated data is limited.

CHEN Zerui

Research Intern

Inria

zerui.chen [at] inria.fr

Short bio

Master Degree (University of Chinese Academy of Sciences), Bachelor Degree (Northwestern Polytechnical University)

Research topic

Learning policies for object manipulation from real videos

Short abstract

Learning accurate policies for diverse tasks and environments is a long-standing challenge in computer vision and robotics. The goal of this project is to learn policies for object manipulation by reconstructing and modeling hands and objects in real videos with people performing related actions.

MAKAROFF Nicolas

PhD Student

Université Paris Dauphine-PSL

makaroff [at] ceremade.dauphine.fr

Short bio

M2 MVA ENS Paris-Saclay

Thesis title

Segmentation and modeling of tree structure by Deep Learning with geometric constraints, applications in biomedical imaging.

Short abstract

Deep Learning has shown real capabilities for different tasks ranging from classification to segmentation in various fields such as chemistry, computer vision or even medicine.  Generally, known architectures are used  to  solve  these  problems.   However, this generality of architectures, although obtaining good results, does not yet consider the geometric and topological structure of the studied data, which can lead to a reduced interpretability and acceptability of the results due to a lack of transparency

MARCHAND Tanguy

Postdoctoral Researcher

L’Ecole normale supérieure - PSL

Tanguy.marchand [at] ens.fr

Short bio

Master from Ecole Polytechnique

Master from University of Cambridge

PhD from Sorbonne Université

Research project

Simulating physical stochastic processes using Machine Learning.

Short abstract

Physical stochastic processes such as turbulence, astrophysical maps and so on provide a wide range of non-Gaussian processes. My research is to develop new Machine Learning tools to better analyze and reproduce them.

LE MOING Guillaume

PhD Student

INRIA

guillaume.le-moing [at] inria.fr

Short bio

Master’s degree in Science and Executive Engineering, Mines ParisTech (diplôme d’ingénieur)

Master’s degree in Artificial Intelligence and Data-Science (IASD), PSL Research University

Thesis title

Learning robust representations for improved visual understanding.

Short abstract

Recent breakthroughs in the field of computer vision, and in particular those leveraging deep supervised learning, often require large high-quality labeled datasets. In this thesis, we are interested in reducing human supervision during training as well as building robust visual representations from a limited number of annotated samples. We will tackle the data scarcity problem by leveraging data augmentation by looking at both theoretical and practical aspects.

HUERTEVENT Marie

PhD Student

INRIA

marie.huertevent [at] inria.fr

Short bio

Diplôme d’ingénieur, ENPC
Master MVA, ENS Cachan

Thesis title

Fusion of LiDAR, RADAR and RGB data for autonomous vehicle navigation.

Short abstract

We will explore the fusion of multiple sensory information for autonomous vehicles. An autonomous car navigates in uninstrumented real-world environments, and it actively collects desired data in a sample efficient way via a variety of sensors, including cameras, LiDARs, and Radars. To improve perception and navigation in such scenarios, we seek to combine weakly/self-supervised learning with multi-modal learning techniques.

FU Changqing

PhD Student

Dauphine - PSL

cfu {at] ceremade.dauphine.fr

Short bio

Msc in Theoretical and Applied Mathematics, Dauphine – PSL

Bsc in Mathematics, Fudan University

Thesis title

Evaluation of Generative Adversarial Networks.

Short abstract

Generative Adversarial Network (GAN) has been an important algorithm for image generation/ translation in recent years. Finding better quantitative measures to avoid overfitting and combining traditional methods with neural networks are among various lines of research. Application involves medical images and privacy preservation of image data.

SCHMID Cordelia

Computer Vision

cordelia.schmid [at] inria.fr / Twitter: @cordeliaschmid

Cordelia Schmid

Short bio

Cordelia Schmid is a research director at Inria Grenoble. Longuet- Higgins / Koenderink award for fundamental contributions in computer vision (2006, 2014, 2016, 2018). Fellow IEEE (2012). ERC advanced grant (2013). Humbolt research award (2015). Inria & French Academy of Science Grand Prix (2016). German National Academy of Sciences, Leopoldina (2017). Recipient of the Royal Society Milner Award (2020).

Topics of interest

Computer vision, machine learning, robotics

Project in Prairie

Machine perception has made significant progress over the past decade. There are now readily available models for tasks such as object detection, semantic segmentation and video classification. Our goal is to move towards high-level visual representations with an emphasis on autonomous learning for next generation AI systems.

Quote

Today’s systems are still surprisingly dependent on annotated data and the goal here is to design methods that learn independently without or with only sparse supervision and demonstrate evidence of autonomy Furthermore, learning should not be only static, but be based on the interaction with the world. We will pursue two main directions. The first one is on learning without or weak annotations given multi-modal data. The second one goes one step further and interacts with the world in order to learn without supervision, here without loss of generality based on a robot arm.

PONCE Jean

Computer Vision

jean.ponce [at] inria.fr

Jean Ponce

Short bio

Professor and former director, Department of Computer Science (on leave at Inria since Fall 2017), Ecole normale superieure. Distinguished Visiting Faculty, NYU (Fall 2017-). IEEE Fellow (2003). Recipient of an ERC Sr. Grant (2011) and the CVPR and ICML test-of-time awards (2016 and 2019). Sr. Editor-in-Chief, International Journal of Computer Vision (2019-).

Topics of interest

Computer vision, image processing, machine learning, robotics

Project in Prairie

Jean Ponce will address scale and supervision issues in vision, visually guided robotics, the NLP/vision interface, biological image restoration, cultural heritage preservation, and “blue-sky” collaborations with industry in vision and robotics. He will participate in the PSL AI graduate school, and develop a reference annual AI summer school.

Quote

Today’s computer vision technology is quite good at identifying animals, people, or natural and man-made objects in cluttered images and videos. But it relies on a humongous amount of manual annotation to learn the corresponding visual models. The vision systems of tomorrow will have to continuously learn from data with a much weaker level of human supervision, to adapt to new users for digital assistants or new routes and driving conditions for autonomous cars, and truly leverage the billions of images available on the Internet. This change of paradigm is necessary for the successful large-scale deployment of computer vision technology, and it is a central scientific challenge for our field.

LAPTEV Ivan

Computer Vision

ivan.laptev [at] inria.fr

Ivan Laptev

Short bio

Senior researcher at INRIA Paris and head of scientific board at VisionLabs, holds PhD degree from the Royal Institute of Technology (2004) and HDR degree from Ecole Normale Superieure (2013). Recipient of an ERC Starting Grant (2012) and an ICCV Helmholtz prize (2017). Program chair for CVPR (2018) and associate editor of IJCV and TPAMI journals.

Topics of interest

Computer vison, robotics, machine learning

Project in Prairie

Ivan Laptev will address the synergy between computer vision, robotics and natural language understanding. He will focus on learning embodied visio-linguistic representations for robotics exploring methods such as deep imitation, reinforcement learning and weakly-supervised learning for transferring knowledge from human demonstrations and instructions. He will collaborate with industrial partners and teach classes at the MVA Mater program.

Quote

Deep neural networks and machine learning have recently revolutionized computer vision. Typical proxy tasks such as object recognition and semantic image segmentation have achieved maturity. Yet, this progress so far had only limited influence on robotics. While perception and vision in particular are crucial components of robotics, traditional robotics methods typically decouple perception from control. With the advances in deep learning, an integrated approach of learning visual representations together with control functions now gives an opportunity for a breakthrough in the field. My goal in PRAIRIE is to bring advances of computer vision and natural language processing to robotics. While supervised learning has been crucial for the progress in computer vision, full supervision is rarely available for robotics tasks. Overcoming this limitation will be a major scientific challenge of the project.