Research - NAVER LABS Europe
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RESEARCH

Our technical expertise is enriched with a deep understanding of how people interact with technology.

Some of the big cross-disciplinary challenges we address:

Representation learning: turning all data entities (text, images, videos, web pages, emails, …) into semantically-rich representations to that can be efficiently processed, mined and related to each other.

Interactive learning: including the human in the learning loop to benefit from the complementary capabilities of humans and machines

Lifelong learning: exploring how to learn and manage prediction systems, policies or processes in a sustainable manner, i.e. in a manner that makes maximum reuse of previous experiences.

Machine reasoning: connecting existing pieces of knowledge to create new knowledge, e.g. being able to answer complex questions that require multiple supporting facts.

Decision making: learning how to make actionable decisions in uncertainty, especially in ever-changing environments

Agility: we live in an ever-changing world which means the technology we use must evolve in synergy with the people and business processes that touch it.

User centric: an important challenge in any technology is to the adoption by people. Only then does science become innovation. Our solutions are grounded in a deep understanding of how people work and how technology can help them every day.

Collaboration

Our scientists collaborate with national and international partners, academics and businesses to solve problems and invent technology and services that will have impact in the real world.

Discover how our research is recognized.

Research illustrating image woman and 2 man in front of computers
12 June 2019

“I Don’t Understand…” Issues in Self-Quantifying Commuting

This paper illustrates the results of a study that we conducted on the effectiveness of self-tracking of commuting data where participants received daily feedback on the financial costs and CO2 emissions associated to their mobility practices.
29 May 2019

Improving the Generalization of Visual Navigation Policies using Invariance Regularization

Training agents to operate in one environment often yields overfitted models that are unable to generalize to the changes in that environment.
27 May 2019

Visual Localization by Learning Objects-of-Interest Dense Match Regression

We introduce a novel CNN-based approach for visual localization from a single RGB image that relies on densely matching a set of Objects-of-Interest (OOIs).
10 July 2019
SIGIR 2019 cover

The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France

21st - 25th July 2019
We are a SILVER sponsor of SIGIR.
2 July 2019
ICVSS 2019 cover

International Computer Vision Summer School 2019 (ICVSS), Sicily, Italy

7th - 13th July 2019We are GOLD sponsor of ICVSS.
30 June 2019
HCI 2019 cover

The 21st International Conference on Human-Computer Interaction (HCI International 2019), Orlando, Florida, USA

HCI International 2019: 26th - 31th July 2019
10 July 2019
ICML 2019 review blog

ICML 2019 Highlights

A recap with analysis of what caught our attention most at this year's conference.
14 June 2019
Augmented Reality Guide cover image

An Augmented Reality Guide for Museums

The acme of perfection of AR is when you can’t distinguish the virtual from the real. We experiment with AI and computer vision in our AR museum guide ARAO to make the physical experience a natural one.
14 June 2019
Making maps evergreen with deep learning, robots and computer vision

Making maps evergreen with deep learning, robots and computer vision

A system that correctly detects when places have changed to automatically update complex indoor maps. Dataset available for research.

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