Internships

We are looking for students finishing their MSc with a solid background in computer vision and machine learning, particularly in deep learning with strong PyTorch coding skills. Interns work on research topics, typically for 6 months (usually spring and summer), resulting for a great part in paper submissions to top-tier conferences. Some trainees go on to do a PhD thesis in the lab.

Alumni interns and visiting students

Achraff Adjileye - Internship 2024
Currently: PhD student
Baptiste Callard - Internship 2024
Valentin Gerard - Internship 2024
Currently: PhD student
Ellington Kirby - Internship 2024
Currently: Computer Vision engineer at Parrot
Matteo Marengo - Internship 2024
Currently: Research Intern at Harvard Medical School
Walter Simoncini - Visiting Master student (April 2024 - May 2024)
Currently: PhD student at University of Technology Nuremberg with Yuki Asano
Monika Wysoczanska - Visiting PhD student (July 2023 - June 2024)
Currently: Internship at Google Deepmind with Cordelia Schmid
Amaia Cardiel - Internship 2023
Currently: PhD student at Valeo.ai and Grenoble Université with Eric Gaussier
Sophia Sirko-Galouchenko - Internship 2023
Currently: PhD student at Valeo.ai and Sorbonne Université with Nicolas Thome
Pegah Khayatan - Internship 2023
Currently: Visiting student at Sorbonne Université with Matthieu Cord
Thibaut Loiseau - Internship 2023
Currently: PhD student at ENPC with Vincent Lepetit
Denis Mbey Akola - Internship 2023
Currently: PhD student at NYU with David Fouhey
Loïck Chambon - Internship 2022
Currently: PhD student at Valeo.ai and Sorbonne Université with Matthieu Cord
Mehdi Zemni - Internship 2022
Currently: Data scientist at Argon & co
Chloé Sekkat - Internship 2022
Currently: Machine Learning Engineer at Sonos
Victoria Brami - Internship 2022
Currently: Visiting student at the University of Edinburgh
Angelika Ando - Internship 2022
Currently: PhD student in Mines wtih Fabien Moutarde and Bogdan Stanciulescu
Corentin Sautier - Internship 2021
Currently: PhD student at Valeo.ai and ENPC with Vincent Lepetit
Imen Maazoun - Internship 2021
Currently: Machine Learning Engineer at Monk AI
Paul Jacob - Internship 2021
Currently: Research scientist at Mistral AI. Previously Research scientist at Owkin
Anh-Quan Cao - Internship 2020
Currently: PhD student at INRIA with Raoul de Charette
Björn Michele - Internship 2020
Currently: PhD student at Valeo.ai and IRISA OBELIX with Nicolas Courty
Guillaume Le Moing - Internship 2020
Currently: Research scientist at Google Deepmind. PhD at INRIA with Jean Ponce and Cordelia Schmid
Victor Besnier - Internship 2019
Currently: Research scientist at valeo.ai. PhD at Valeo and ENPC with David Picard
Florent Bartoccioni - Internship 2019
Currently: Research engineer at valeo.ai. PhD at Valeo.ai and INRIA with Karteek Alahari.


Projects led or contributed to by interns

  1. Test-Time Contrastive Concepts for Open-World Semantic Segmentation
    Monika Wysoczańska, Antonin Vobecky, Amaia Cardiel, Tomasz Trzciński, Renaud Marlet, Andrei Bursuc, Oriane Siméoni
    under review, 2025
    pdf
  2. CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation
    Monika Wysoczańska, Oriane Siméoni, Michaël Ramamonjisoa, Andrei Bursuc, Tomasz Trzciński, Patrick Pérez
    European Conference on Computer Vision, 2024
    pdf Code
  3. Reliability in Semantic Segmentation: Can We Use Synthetic Data?
    Thibaut Loiseau, Tuan-Hung Vu, Mickael Chen, Patrick Pérez, Matthieu Cord
    European Conference on Computer Vision, 2024
    pdf Code
  4. LLM-wrapper: Black-Box Semantic-Aware Adaptation of Vision-Language Foundation Models
    Amaia Cardiel, Éloi Zablocki, Oriane Siméoni, Elias Ramzi, Matthieu Cord
    ECCV Workshop Emergent Visual Abilities and Limits of Foundation Models (EVAL-FoMo), 2024
    pdf
  5. ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable
    Yuan Yin, Pegah Khayatan, Éloi Zablocki, Alexandre Boulch, Matthieu Cord
    ECCV Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving (W-CODA), 2024
    pdf Code
  6. OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
    Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris, Andrei Bursuc, Antonin Vobecky, Patrick Pérez, Renaud Marlet
    CVPR Workshop on Autonomous Driving (WAD), 2024
    pdf Code
  7. CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free
    Monika Wysoczańska, Michaël Ramamonjisoa, Tomasz Trzciński, Oriane Siméoni
    Winter Conference on Applications of Computer Vision (WACV), 2024
    pdf Code
  8. Unsupervised Object Localization: Observing the Background to Discover Objects
    Oriane Siméoni, Chloé Sekkat, Gilles Puy, Antonin Vobecky, Éloi Zablocki, Patrick Pérez
    Computer Vision and Pattern Recognition, 2023
    pdf Code
  9. OCTET: Object-aware Counterfactual Explanations
    Mehdi Zemni, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord
    Computer Vision and Pattern Recognition, 2023
    pdf Code
  10. RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
    Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, and Renaud Marlet
    Computer Vision and Pattern Recognition, 2023
    pdf Code
  11. Multi-Modal 3D GAN for Urban Scenes
    Loïck Chambon, Mickaël Chen, Tuan-Hung Vu, Alexandre Boulch, Andrei Bursuc, Matthieu Cord, Patrick Pérez
    NeurIPS Machine Learning for Autonomous Driving Workshop, 2022
    pdf Slides
  12. STEEX: Steering Counterfactual Explanations with Semantics
    Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord
    European Conference on Computer Vision, 2022
    pdf Code
  13. Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
    Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet
    Computer Vision and Pattern Recognition, 2022
    pdf Code
  14. Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds
    Bjoern Michele, Alexandre Boulch, Gilles Puy, Maxime Bucher, and Renaud Marlet
    International Conference on 3D Vision (3DV), 2021
    pdf Code Slides
  15. PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
    Anh-Quan Cao, Gilles Puy, Alexandre Boulch, and Renaud Marlet
    International Conference on Computer Vision (ICCV), 2021
    pdf Code
  16. Semantic Palette: Guiding Scene Generation with Class Proportions
    Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain, Patrick Pérez and Matthieu Cord
    Computer Vision and Pattern Recognition, 2021
    pdf Code