A year has already passed since NAVER LABS Europe was born. Here we look at some of our highlights which combine lots of machine learning with lots of learning about NAVER, NAVER LABS, Korean culture and even quite a bit of learning about ourselves as we determined where we want to be a few years from now and how we’re going to get there.
Summer is a time of celebration
Our inauguration was during the beautiful French summer of 2017. We had a welcome party with NAVER senior executives including Founder Hae-jin Lee and CEO Ms Seong-sook Han here in our LABS in Grenoble.
Although we couldn’t meet all our peers in Korea they sent us a bunch of ‘Welcome aboard’ cards and made us a fabulously kind welcome video.
CVPR 2017 was our first joint international scientific conference with the Korean and European team together. LABS was a platinum sponsor. Below is a photo of us on the booth. This was the beginning of a whole series of sponsorships where we worked together to tell the world about NAVER LABS and to help grow our teams on both sides of the world. LABS is developing in Korea and internationally.
DEVIEW in the fall
We had our 1st ‘DEVIEW experience’ in Seoul. DEVIEW is Korea’s leading annual tech conference by NAVER where CTO and NAVER LABS CEO Chang Song unveils new ambient intelligence products and technologies in his keynote [Kr] and the rest of the two days is packed with presentations on AI, Ambient Intelligence, machine learning etc. It takes place in Seoul’s massive COEX complex. Tickets are free and the 2500 seat event is generally sold out within a minute of being available online. NAVER LABS Europe gave 4 presentations on computer vision, machine reading and optimization which you can view below.
After DEVIEW we spent the rest of the week visiting our colleagues in LABS and other parts of NAVER in their Green Factory HQ named as such because of its low energy use and eco materials. For the HQ of Korea’s leading internet services portal it has a ground floor open to the general public where people walk in off the streets to grab a coffee and browse freely (in every sense of the word) the thousands of magazines and books in the café and library. It’s a refreshing difference from the top security no entry attitude of many of their foreign counterparts. Here the mantra is ‘knowledge sharing’ 🙂 We ate lots of Korean BBQ so the vegetarians had a bit of a hard time.
Winter
Michel Gastaldo became director of the LABS at the beginning of the year. It’s at that time we changed how we were set up in the LABS to best realise our vision. Our scientific director, Florent Perronnin talks a bit about this and what kind of place we now are in his article ‘NAVER LABS Europe, the start of something big and an invitation to come join us.
We began to give a series of seminars at start-up collaborative Space Green which is the NAVER/LINE space that now houses about 15 start-ups in the world’s largest start-up campus Station F in Paris.
Station F is the world’s largest startup campus where NAVER LINE is one of the biggest partners with 80 seats. NAVER LABS Europe scientists regularly give seminars that are filmed for the public. Below are some recent talks.
Spring (and summer)
We became a founding member of PRAIRIE (Paris Artificial Intelligence Research Institute). The announcement came right after the French President’s event on AI for Humanity where he presented the country’s the five-year strategy to make France and Europe a leader in ethical AI. As France’s biggest industrial AI research organization it may be obvious why we decided to be part of this but you can read the story by Florent here.
While we joined PRAIRIE 10 new talented scientists chose to join us and we’re expecting quite a few more in the months to come. If you’re interested you can see who we are here or check out our positions.
We started our 2018 sponsorship programme. You may have bumped into us at ECIR or SMAI-MODE both in Grenoble, The Web conference in Lyon or Document Analysis Systems (DAS) at IAPR in Vienna in April. In May we sponsored the SE4COG workshop at ICSE in Sweden. In June we were back at CVPR at the main conference as a silver sponsor but also at the workshops on Embedded Vision and 3D Humans (read the blog series on CVPR). We also sponsored the best paper at ECSCW in Nancy and were gold sponsor at ICML in Sweden at the beginning of July. We had papers at most of these conferences plus others at EclipseCon, ICDAR, IEEE ICTAI, ICDM, ALT2018, AISTATS2018, AAAL 2018, Coria TALN, CHI, LREC and CAp.
With INRIA we co-organized PAISS, the first Artificial Intelligence Summer School of PRAIRIE. With over 200 participants this was quite a bit of work throughout the whole of the spring and right up to the event beginning of July. We had some of the world’s leading personalities in AI speaking at the event which was considered hugely successful thanks to the quality of the lectures but also the opportunities to mingle and discuss at the informal social events.
What’s coming next
In August we’ll be giving a paper at the International Ergonomics Association (IEA) in Florence and in early September we’ll be out in force with a dozen of us going to ECCV in September where we’re Platinum sponsor of the conference, Silver sponsor of WiCV and sponsor of TASK-CV. We’ll be presenting 2 papers there. Come meet us and discover the surprise French goodie on the booth!
DEVIEW 2018: We’ll be presenting and demonstrating some of our research work from the past year at this fall’s DEVIEW event. This will be our first on stage presence apart from scientific conferences since we became NAVER LABS last year – no pressure please! We have to keep things under cover until then but we’ll be communicating live from the event in Seoul on October 10th and 11th.
In November we’ll be in Brussels at EMNLP where we’re Platinum sponsor and look out for us in December in Montreal at NIPS where we’re a gold sponsor. We hope we’ll see you there….or even here![/vc_column_text][/vc_column][/vc_row]
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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.
——————-
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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