Main conference sessions: June 17-21, Seattle, USA
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses.
Cross-view and cross-pose completion for 3D human understanding
Matthieu Armando, Salma Galaaoui, Fabien Baradel, Thomas Lucas, Vincent Leroy, Romain Brégier, Philippe Weinzaepfel, Gregory Rogez
Wednesday, June 19th, 10:30 – Poster Session 1 & Exhibit Hall
Task-conditioned adaptation of visual features in multi-task policy learning
Pierre Marza, Laetitia Matignon, Olivier Simonin, Christian Wolf
Thursday, June 20th, 17:00 – Poster Session 4 & Exhibit Hall
Learning to navigate efficiently and precisely in real environments
Guillaume Bono, Hervé Poirier, Leonid Antsfeld, Gianluca Monaci, Boris Chidlovskii, Christian Wolf
Thursday, June 20th, 17:00 – Poster Session 4 & Exhibit Hall
SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection
Mingxuan Liu, Tyler Hayes, Elisa Ricci, Gabriela Csurka, Riccardo Volpi
Thursday, June 20th, 17:00 – Poster Session 4 & Exhibit Hall
Label propagation for zero-shot classification with vision-language models
Vladan Stojnic, Yannis Kalantidis, Georgios Tolias
Friday, June 21st, 10:30 – Poster Session 5 & Exhibit Hall
DUSt3R: Geometric 3D Vision Made Easy
Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, Jérome Revaud
Friday, June 21st, 10:30 – Poster Session 5 & Exhibit Hall
Workshop on Image Matching: Local Features & Beyond: Room – Summit 323
14:45-15:00 Invited speaker: Vincent Leroy, DUSt3r and MASt3R – Geometric 3D vision made easy.
3DMV: Learning 3D with Multi-View Supervision workshop: Room – Summit 331
First Joint Egocentric Vision (EgoVis) Workshop: Room – Summit 428
11:20-11:50 Invited speaker: Diane Larlus, 3D understanding in egocentric vision
New Challenges in 3D Human Understanding: Arch Building Exhibit Hall, Boards 1-25
12:30-13:30 Invited poster: Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot, Philippe Weinzaepfel
Second workshop on Reconstruction of Human-Object Interactions (RHOBIN): Room – Summit 427
14:30-15:15 3D human reconstruction track & challenge: ***Winner of the challenge with Multi-HMR***
Workshop on Continual Learning in Computer Vision (CLVISION): Room – Summit 325
Tyler Hayes, co-organizer.
Towards 3D Foundation Models: Progress and Prospects: Room – Summit 434
15:45-16:15 Invited speaker: Jerome Revaud, From CroCo to DUSt3R: A Paradigm Change in 3D Vision?
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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