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An international team based in Paris, which conducts AI research for Valeo automotive applications, in collaboraton with world-class academics. Our main research is on reliable and sustainable automotive AI. See our papers, projects, codes, posts and tweets.


Florent Bartoccioni, research engineer | page
Victor Besnier, research scientist | scholar
Alexandre Boulch, research scientist | page | scholar| github | twitter
Andrei Bursuc, senior scientist | page | scholar | github | twitter
Laura Calem, PhD student w/ CNAM | github | twitter
Loïck Chambon, PhD student w/ Sorbonne U. | linkedin
Mickaël Chen, research scientist | page | scholar | github
Matthieu Cord, principal scientist (and prof. at Sorbonne U.) | page | scholar | twitter
Spyros Gidaris, research scientist | scholar | github
David Hurych, research scientist | scholar
Victor Letzelter, PhD student w/ Telecom Paris | linkedin
Renaud Marlet, principal scientist (and researcher at ENPC) | page | scholar
Björn Michele, PhD student w/ U. Bretagne Sud) | scholar
Patrick Pérez, scientific director (and Valeo VP of AI) | page | scholar
Cédric Rommel, research scientist | page | scholar | twitter
Gilles Puy, research scientist | page | scholar
Nermin Samet, research scientist | page | scholar
Corentin Sautier, PhD student w/ ENPC | linkedin
Oriane Siméoni, research scientist | page | scholar | github
Eduardo Valle, senior scientist | scholar
Antonín Vobecký, PhD student w/ CTU | page | scholar | github
Tuan-Hung Vu, research scientist | page | scholar | github | twitter
Yihong Xu, research scientist | scholar
Eloi Zablocki, research scientist | scholar | twitter
Léon Zheng, PhD student w/ ENS Lyon | page

Human Resource Partner: Marine Michaud
Collaborative projects manager: Serkan Odabas
Assistant: Ouafa Bakrine
Location: 100 rue de Courcelles, Paris

Academic partners

CTU, Prague (Josef Sivic)
EPFL, Lausanne (Alexandre Alahi)
ENPC, Paris (Vincent Lepetit)
ENS & Inria, Lyon (Rémi Gribonval)
Inria Paris (Raoul de Charette)
Sorbonne, Paris (Matthieu Cord, Nicolas Thome)
Télécom, Paris (Gaël Richard, Slim Essid, Mathieu Fontaine)
UBS, Vannes (Nicolas Courty)

Lastest News

[09/2023] team participates in mass to ICCV in Paris, from interns to senior researchers, spot our dark green hoodies and engage with us!
[09/2023] paper Four papers accepted at NeurIPS’23
[07/2023] paper code Five papers accepted at ICCV’23, one with code so far (WaffleIron)
[07/2023] service Renaud Marlet and Oriane Siméoni join ICCV’23 organization as Logistic Chair (Patrick Pérez serving as Industrial Relation co-chair)
[06/2023] pres Oriane Siméoni runs “Object localization for free: Going beyond self-supervised learning” CVPR’23 Tutorial
[06/2023] pres Nine researchers participate to CVPR’23
[06/2023] pres Patrick Pérez delivers keynote at Vision4AllSeasons CVPR’23 workshop
[06/2023] team Florent Bartoccioni defends his PhD at Inria-Grenoble and joins as research engineer
[05/2023] service Andrei Bursuc, Gilles Puy and Spyros Gidaris are Outstanding Reviewers at CVPR’23 [04/2023] presBRAVO: Robustness and Reliability of Autonomous Vehicles in the Open-world” accepted as ICCV’23 workshop
[04/2023] presUncertainty Quantification for Computer Vision” accepted as ICCV’23 wokrshop
[04/2023] pres “The Many Faces of Reliability of Deep Learning for Real-World Deployment” accepted as ICCV’23 tutorial
[03/2023] team celebrates its 5th anniversary!
[02/2023] paper code Six papers accepted at CVPR’23, four with codes (FOUND, ALSO, Range-ViT, OCTET)
[01/2023] team Eduardo Valle starts as senior scientist
[12/2022] team Cédric Rommel starts as research scientist
[11/2022] team Huy Van Vo defends his PhD at ENS and joins FAIR at META
[10/2022] collab Astra-Vision, part of Inria-Valeo joint team Astra, starts research on perception
[10/2022] team Yihong Xu starts as research scientist
[10/2022] paper code data Six papers presented at ECCV’22, five with codes (BiB, D&S, STEEX, AttMask, LDU) and one with dataset (HULC)
[10/2022] pres Andrei Bursuc and Spyros Gidaris run Self-supervision on wheels: Advances in self-supervised learning from autonomous driving data ECCV’22 Tutorial
[09/2022] challenge ObsNet ranks first on SegmentMeIfYouCan benchmark (Anomaly Track) among methods with no OoD training data

Main research themes

Multi-sensor scene understanding and forecasting — Driving monitoring and automatization relies first on a variety of sensors (cameras, radars, laser scanners) that deliver complementary information on the surroundings of a vehicle and on its cabin. Exploiting at best the outputs of each of these sensors at any instant is fundamental to understand the complex environment of the vehicle and to anticipate its evolution in the next seconds. To this end, we explore various machine learning approaches where sensors are considered either in isolation or collectively.

Data/annotation-efficient learning — Collecting diverse enough real data, and annotating it precisely, is complex, costly, time-consuming and doomed insufficient for complex open-world applications. To reduce dramatically these needs, we explore various alternatives to full supervision, in particular for perception tasks: self-supervised representation learning for images and point clouds, visual object detection with no or weak supervision only, unsupervised domain adaptation for semantic segmentation of images and point clouds, for instance. We also investigate training with fully-synthetic or generatively-augmented data.

Dependable models — When the unexpected happens, when the weather badly degrades, when a sensor gets blocked, embedded safety-critical models should continue working or, at least, diagnose the situation to react accordingly, e.g., by calling an alternative system or human oversight. With this in mind, we investigate ways to assess and improve the robustness of neural nets to perturbations, corner cases and various distribution shifts. Making their inner workings more interpretable, by design or in a post-hoc way, is also an important and challenging venue that we explore towards more trust-worthy models.

Code and data

POCO Point Convolution for Surface Reconstruction (CVPR’22)
SFRIK Self-supervised learning with rotation-invariant kernels (ICLR’23)
FOUND Observing the background to discover objects (CVPR’23)
ALSO Automotive Lidar self-supervision by occupancy estimation (CVPR’23)
RangeViT ViT for 3D Semantic Segmentation in autonomous driving (CVPR’23)
OCTET Object-aware counterfactual explanations (CVPR’23)
WaffleIron Automotive point cloud semantic segmentation (ICCV’23)
LidarTouch Monocular metric depth estimation with a few-beam LiDAR (CVIU 2022)
DenseMTL Cross-task attention mechanism for dense multi-task learning (WACV’23)
LaRa Latents and rays for multi-camera BEV semantic segmentation (CoRL’22)
BiB Active learning strategies for weakly-supervised object detection (ECCV’22)
D&S Unsupervised semantic segmentation of urban scenes via cross-modal distillation (ECCV’22)
STEEX Steering Counterfactual Explanations with Semantics (ECCV’22)
CAB Raising Context Awareness in Motion Forecasting (workshop CVPR’22)
MuHDi Multi-head distillation for continual unsupervised domain adaptation in semantic segmentation (CLVision’22)
DIVA Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints (ICPR 2022)
SLidR Image-to-Lidar self-supervised distillation for autonomous driving data (CVPR’22)
RADIal (dataset) HR radar dataset (+ camera & lidar) for vehicle and free space detection (CVPR’22)
LOST Object localization with self-supervised transformers (BMVC’21)
MTAF Multi-Target Adversarial Frameworks for domain adaptation (ICCV’21)
PCAM Product of Cross-Attention Matrices for rigid registration of point clouds (ICCV’21)
SP4ASC Separable convolutions for acoustic scene classification in DCASE’21 Challenge
MVRSS Multi-view radar semantic segmentation (ICCV’21)
ObsNet Out-Of-Distribution detection by learning from local adversarial attacks in semantic segmentation (ICCV’21)
Semantic Palette Guiding scene generation with class proportions (CVPR’21)
Attributes with Fields Detecting 32 pedestrian attributes with composite fields (T-ITS)
OBoW Online BoW generation for unsupervised representation learning (CVPR’21)
DummyNet Artificial Dummies for Urban Dataset Augmentation (AAAI’21)
CARRADA (dataset) Camera and Automotive Radar with Range-Angle-Doppler Annotations dataset (ICPR’20)
ESL Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation (workshop CVPR’20)
FLOT Scene flow on point clouds guided by optimal transport (ECCV’20)
AdamSRT Adam exploiting BN-induced pherical invariance of CNN (arXiv 2020)
LightConvPoint Convolution for points (ACCV’20)
xMUDA Cross-modal UDA for 3D semantic segmentation (CVPR’20)
LearningByCheating End-to-End driving using implicit affordances (CVPR’20)
rOSD Unsupervised object discovery at scale (ECCV’20)
ConvPoint Convolutions for unstructured point clouds (Computer \& Graphics 2020)
BEEF Driving behavior explanation with multi-level fusion (workshop NeurIPS’20 and Pattern Recognition’22)
Woodscape Driving fisheye multi-task dataset (ICCV’19)
ZS3 Zero-Shot Semantic Segmentation (NeurIPS’19)
BF3S Boosting few-shot visual learning with self-supervision (ICCV’19)
ConfidNet Addressing failure prediction by learning model confidence (NeurIPS’19)
Rainbow-IQN Ape-X effective RL combination for Atari games
DADA Depth-aware Domain Adaptation in Semantic Segmentation (ICCV’19)
AdvEnt Adversarial Entropy minimization for domain adaptation in semantic segmentation (CVPR’19)
OSD Unsupervised object discovery as optimization (CVPR’19)

Previous news



Hédi Ben-younes, research scientist (scholar), now at BCG
Maxime Bucher, research scientist (scholar)
Charles Corbière, PhD student with CNAM (scholar), now at EPFL
Maximilian Jaritz, PhD student (scholar), now at Amazon
Himalaya Jain, research scientist (scholar), now at Helsing
Gabriel de Marmiesse, research engineer (github), now at Preligens
Arthur Ouaknine, PhD student with Telecom (scholar), now at McGill
Julien Rebut, research scientist (scholar), now at Hexagon|AutonomouStuff
Simon Roburin, PhD student with Ponts (scholar)
Antoine Saporta, PhD student with Sorbonne U. (scholar), now at Meero
Tristan Schultz, research engineer, now at Valeo IS Direction
Marin Toromanoff, PhD student (scholar), now at Valeo Driving Assistance Research
Huy Van Vo, PhD student with Inria (scholar), now at Meta
Emilie Wirbel, research scientist (scholar), now at Nvidia