11th – 17th October 2021, virtual event
ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials.
Below are the NAVER, NAVER LABS Europe and LINE papers and workshop activities at this year’s conference.
We are an ICCV supporter and sponsor.
10:00-11:30 EDT | 16:00-17:45 CEST | 23:00-00:30 KST
Workshop on Embedded and Real-World Computer Vision in Autonomous Driving (ERCVAD 2021)
Gabriela Csurka, keynote speaker ‘Visual DA in autonomous driving scenarios’
Tuesday 12:00-13:00 EDT | 18:00-19:00 CEST | 01:00-02:00 (+1 day) KST
Thursday 19:00-20:00 EDT | 01:00-02:00 (+1 day CEST ) | 08:00-09:00 (+1 day) KST
Normalization Matters in Weakly Supervised Object Localization
Jeesoo Kim, Junsuk Choe, Sangdoo Yun, Nojun Kwak
[pdf] [supp] [arXiv]
Contrastive Attention Maps for Self-supervised Co-localization
Minsong Ki, Youngjung Uh, Junsuk Choe, Hyeran Byun
[pdf] [supp]
Tuesday 15:00-16:00 EDT | 21:00-22:00 CEST | 04:00-05:00 (+1 day) KST
Thursday 08:00-09:00 EDT | 14:00-15:00 CEST | 21:00-22:00 KST
Robust and automatic monocular vehicle speed estimation for traffic surveillance
Jerome Revaud, Martin Humenberger
[pdf] [supp]
Tuesday 15:00-16:00 EDT | 21:00-22:00 CEST | 04:00-05:00 (+1 day) KST
Thursday 08:00-09:00 EDT | 14:00-15:00 CEST | 21:00-22:00 KST
Generalized Shuffled Linear Regression (Oral)
Feiran Li, Kent Fujiwara, Fumio Okura, Yasuyuki Matsushita
[pdf] [supp]
On the Limits of Pseudo Ground Truth in Visual Camera Re-Localization
Eric Brachmann; Martin Humenberger; Carsten Rother; Torsten Sattle
[pdf] [supp] [arXiv]
Wednesday 08:00-09:00 EDT | 14:00-15:00 CEST | 21:00-22:00 KST
Friday 15:00-17:00 EDT | 21:00-23:00 CEST | 04:00-06:00 (+1 day) KST
Keep CALM and Improve Visual Feature Attribution
Jae Myung Kim, Junsuk Choe, Zeynep Akata, Seong Joon Oh.
[pdf] [supp] [arXiv]
Wednesday 09:00-10:00 EDT | 15:00-16:00 CEST | 22:00-23:00 KST
Friday 16:00-18:00 EDT | 22:00-00:00 CEST | 05:00-07:00 (+1 day) KST
Concept generalization in visual representation learning
Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus, Karteek Alahari
[pdf] [supp] [arXiv] [project page]
Self-supervised Contrastive Regularization for Domain Generalization
Daehee Kim, Seunghyun Park, Jinkyu Kim, Jaekoo Lee
[pdf] [supp]
Wednesday 10:00-11:00 EDT | 16:00-17:00 CEST | 2300-00:00 KST
Friday 17:00-18:00 EDT | 23:00-00:00 CEST | 06:00-07:00 (+1 day) KST
Learning with Memory-based Virtual Classes for Deep Metric Learning.
Byungsoo Ko, Geonmo Gu, Han-Gyu Kim
[pdf] [supp] [arXiv]
Rethinking Spatial Dimensions of Vision Transformers
Byeongho Heo, Sangdoo Yun, Dongyoon Han , Sanghyuk Chun , Junsuk Choe, Seong Joon Oh
[pdf] [arXiv]
Wednesday 18:00-19:00 EDT | 00:00-01:00 (+1 day) CEST | 07:00-08:00 (+1 day) KST
Friday 11:00-12:00 EDT | 17:00-18:00 CEST | 00:00-01:00 (+1 day) KST
Rethinking the Truly Unsupervised Image-to-Image Translation
Kyungjune Baek, Yunjey Choi, Youngjung Uh, Jaejun Yoo, Hyunjung Shim
[pdf] [supp] [arXiv]
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Song Park, Sanghyuk Chun, Junbum Cha, Bado Lee, Hyunjung Shim
[pdf] [supp] [arXiv]
Wednesday 19:00-20:00 EDT | 00:01-02:00 (+1 day) CEST | 08:00-09:00 (+1 day) KST
Friday 12:00-13:00 EDT | 18:00-19:00 CEST | 01:00-02:00 (+1 day) KST
A Closer Look at Rotation-Invariant Deep Point Cloud Analysis
Feiran Li, Kent Fujiwara, Fumio Okura, Yasuyuki Matsushita
[pdf] [supp]
Workshop on Self-supervised Learning for Next-Generation Industry-Level Autonomous Driving
Learning from Long-Tailed Data with Noisy Labels
Shyamgopal Karthik, Jerome Revaud and Boris Chidlovskii
[pdf]
Afternoon EDT
Long-term visualization workshop (4th Workshop on Long-term Visual Localization under Changing Conditions)
Martin Humenberger co-organiser. NAVER LABS Europe, workshop sponsor
As part of the workshop, we are hosting challenges on (1) outdoor visual localization, (2) indoor visual localization, and (3) place recognition.
The winner and runner-up of each challenge will receive prize money.
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.
—————
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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