Automatically extracting information from images and videos for real life applications based on visual search, 3D vision, human sensing, visual reasoning, camera pose estimation, and lifelong learning.



  • Paper at 2nd Conf on Lifelong Learning Agents – CoLLAs 2023
  • Romain Bregier and Jerome Revaud outstanding reviewers CVPR 2023.
  • Diane Larlus Doctoral Consortium chair and Area Chair ICCV 2023.
  • Tutorial: Visual Recognition Beyond the Comfort Zone at ICCV 2023
  • 4 papers and 2 invited workshop talks at CVPR 2023
  • Spotlight paper at ICLR 2023



Computer Vision

The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.

Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.

Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.

We have 4 research groups in vision: Spatial AI, Deep Geometric Learning, Visual Representation Learning and 3D Humans. Our research combines skills in machine learning, pattern recognition as well as 3D vision, and our research is focused on long-term oriented problems with relevance to current and future NAVER services. We’re very active in the computer vision community and our research is often pursued in collaboration with external partners from academia.

A novel, plug and play model for human 3D shape estimation of the body or hands, in videos which is trained by mimicking the BERT algorithm from the natural language processing community. Blog post by Fabien Baradel, Philippe Weinzaepfel, Romain Brégier, Yannis Kalantidis and Gregory Rogez
Localization Datasets in Crowded Indoor Spaces
NAVER LABS has made available five new indoor datasets for large scale visual localization in crowded public spaces. Blog article by Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guerin, Gabriela Csurka Khedari and Martin Humenberger
Continual Learning of visual representations without catastrophic forgetting
Using domain randomization and meta-learning, computer vision models forget less when exposed to training samples from new domains. Blog article by Riccardo Volpi, Diane Larlus and Grégory Rogez
Methods for visual localization blog image
This article gives an overview of current state-of-the-art methods and their advantages and drawbacks. Blog article by Martin Humenberger, Gabriela Csurka Khedari, Nicolas Guerin and Boris Chidlovskii
Open source release of the structure from motion and visual localization data format kapture. Blog article by Martin Humenberger
Naver Labs Europe is leading a chair on Lifelong Representation Learning as part of the MIAI institute (Multidisciplinary Institute in Artificial Intelligence)
The short memory of artificial neural networks
A research overview of current work in lifelong learning. Blog article by Riccardo Volpi
A first-of-its-kind architecture that, based on a single image, predicts how a robot can pick up objects from within any scene could revolutionize applications in AR/VR and robotics. Blog article by Gregory Rogez

Recent Publications:

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