******************** ECCV 2020 NAVER NEWS ********************
Congratulations to Florent Perronnin (NAVER LABS Europe), Jorge Sanchez, (National University of Cordoba) & Thomas Mensink (University of Amsterdam) for winning the Koenderink 10 year test of time award at ECCV 2020 for their 2010 paper Improving the Fisher kernel for large-scale image classification! The award was announced in the opening ceremony of the conference Monday August 24th.
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NAVER LABS Europe came 2nd in the Long-Term Visual Localization Challenge in Autonomous Vehicles!
Learn more about how NAVER LABS Europe people and their research have been recognized
DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild,
Authors: Philippe Weinzaepfel, Romain Brégier, Hadrien Combaluzier, Vincent Leroy, Gregory Rogez
Learning Visual Representations with Caption Annotations
Authors: Bülent Sariyildiz, Julien Perez, Diane Larlus
Learning to Generate Grounded Visual Captions without Localization Supervision,
Authors: Chih-Yao Ma, Yannis Kalantidis, Ghassan AlRegib, Peter Vajda, Marcus Rohrbach and Zsolt Kira
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction,
Authors: Anil Armagan, Philippe Weinzaepfel, Romain Bregier, Gregory Rogez et al.
Date: Friday 28th August 2020
Tutorial: “Domain Adaptation for Visual Applications“
Speakers: Mathieu Salzmann (EPFL), Gabriela Csurka (NAVER LABS Europe), Tatiana Tommasi (Politecnico di Torino) and Timothy M. Hospedales (University of Edinburgh).
Date: Sunday 23rd August 2020
Workshop: TASK-CV
Organisers: Tatiana Tommasi (Politecnico di Torino, Italy), Antonio M. Lopez (CVC & UAB, Spain), David Vazquez (Element AI, Canada), Gabriela Csurka (NAVER LABS Europe, France), Kate Saenko (Boston University, USA), Liang Zheng (Australian National University, Australia), Xingchao Peng (Boston University, USA), Weijian Deng (Australian National University, Australia)
NAVER LABS Europe is supporting the best paper award at TASK-CV 2020.
Congratulations to: Zhen Zhao, Yuhong Guo, Jieping Ye for their paper: “Bi-Dimensonal Feature Alignment for Cross-Domain Object Detection”.
Workshop: Women in Computer Vision
Date: Sunday 23rd August, 10pm (UTC+1)
Diane Larlus, Principal Scientist, mentor at the mentoring dinner
Workshop: Long-Term Visual Localization
Date: Friday 28th August 2020
NAVER LABS Europe 2nd place in Autonomous Vehicle Challenge: Martin Humenberger, Yohann Cabon, Nicolas Guerin, Julien Morat, Jérôme Revaud, Philippe Rerole, Noe ́ Pion, Cesar de Souza, Gabriela Csurka, Robust Image Retrieval-based Visual Localization using Kapture.
All datasets for this challenge were provided by NAVER LABS Europe’ in the kapture format, a data format and tool set for facilitating the integration of various datasets within visual localization processing environments.
More details on can be found on the kapture website.
Code, specifications, and documentation are on GitHub.
NAVER LABS Europe is a sponsor of the Long-Term Visual Localization Workshop.
Workshop: Instance-Level Recognition Workshop
Date: Friday 28th August, 10pm (UTC+1)
Invited talk: Diane Larlus, Principal Scientist, “From Instance-Level to Semantic Image Retrieval”
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimization problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimization to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments. More details on our research can be found in the Explore section below.
For a robot to be useful it must be able to represent its knowledge of the world, share what it learns and interact with other agents, in particular humans. Our research combines expertise in human-robot interaction, natural language processing, speech, information retrieval, data management and low code/no code programming to build AI components that will help next-generation robots perform complex real-world tasks. These components will help robots interact safely with humans and their physical environment, other robots and systems, represent and update their world knowledge and share it with the rest of the fleet. More details on our research can be found in the Explore section below.
Visual perception is a necessary part of any intelligent system that is meant to interact with the world. Robots need to perceive the structure, the objects, and people in their environment to better understand the world and perform the tasks they are assigned. Our research combines expertise in visual representation learning, self-supervised learning and human behaviour understanding to build AI components that help robots understand and navigate in their 3D environment, detect and interact with surrounding objects and people and continuously adapt themselves when deployed in new environments. More details on our research can be found in the Explore section below.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
Details on the gender equality index score 2024 (related to year 2023) for NAVER France of 87/100.
1. Difference in female/male salary: 34/40 points
2. Difference in salary increases female/male: 35/35 points
3. Salary increases upon return from maternity leave: Non calculable
4. Number of employees in under-represented gender in 10 highest salaries: 5/10 points
The NAVER France targets set in 2022 (Indicator n°1: +2 points in 2024 and Indicator n°4: +5 points in 2025) have been achieved.
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2024 au titre des données 2023 : 87/100
Détail des indicateurs :
1. Les écarts de salaire entre les femmes et les hommes: 34 sur 40 points
2. Les écarts des augmentations individuelles entre les femmes et les hommes : 35 sur 35 points
3. Toutes les salariées augmentées revenant de congé maternité : Incalculable
4. Le nombre de salarié du sexe sous-représenté parmi les 10 plus hautes rémunérations : 5 sur 10 points
Les objectifs de progression de l’Index définis en 2022 (Indicateur n°1 : +2 points en 2024 et Indicateur n°4 : +5 points en 2025) ont été atteints.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
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.
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