BLOG

10 July 2019

Christopher Dance, Arnaud Sors, Quentin Grail - 10 July 2019

ICML 2019 Highlights

A recap with analysis of what caught our attention most at this year's conference.

14 June 2019

Yves Hoppenot - 14 June 2019

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

Jérome Revaud - 14 June 2019

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.

7 June 2019

Nieves CRASTO, Philippe Weinzaepfel - 7 June 2019

MARS: Motion-Augmented RGB Stream for Action Recognition

This blog presents our CVPR’19 paper on “MARS: Motion-Augmented RGB Stream for Action Recognition” done with the Thoth team at Inria.

7 June 2019

Shreepriya Shreepriya, Danilo Gallo, Sruthi Viswanathan, Jutta Willamowski, Tommaso Colombino - 7 June 2019

CHI 2019 – Our highlights from this year’s conference on AI, Robotics, Recommender Systems and Accessibility and Inclusion

The UX and Ethnography team joined the ACM Conference on Human Factors in Computing Systems (CHI), the most important conference in the field of Human-Computing Interaction. In this article we share our highlights and how we lived this intensive event.

27 May 2019

Philippe Weinzaepfel, Gabriela Csurka Khedari, Yohann Cabon, Martin Humenberger - 27 May 2019

Visual Localization by Learning OOIs Dense Match Regression

The first deep regression-based method that generalizes to larger environments, performs better than state-of-the-art with little training data and is robust to occlusion. Virtual Gallery dataset provided.

2 May 2019

Irene Maxwell - 2 May 2019

We’ve got the look! You’re on the new NAVER LABS Europe web site

New web design, new features, new content

28 February 2019

Gabriela Csurka Khedari, Christopher Dance, Martin Humenberger - 28 February 2019

From handcrafted to deep local features for computer vision applications.

An article we recently published on arXiv gives an overview of the evolution of local features - from handcrafted to deep-learning-based methods - and a discussion of several benchmarks and papers that evaluate local features.

10 December 2018