COMPUTER VISION

The computer vision team conducts research in a wide range of areas, including visual search, scene parsing, human sensing, action recognition, pose estimation and lifelong learning.

Highlights

2021

Co-organizing the ‘ImageNet: past, present and future’ workshop at NeurIPS 2021
Paper accepted at ICCV 2021, Concept Generalization in Visual Representation Learning
The team has 3 papers accepted at CVPR 2021 including an oral, and a findings paper in the Continual Learning workshop.
Paper accepted at ICLR2021 on progressive skeletonization – network pruning at initialization (on openreview)
Paper with IRI & Univ. Aalto on multi-finger grasping accepted at ICRA 2021 (arXIv preprint)

2020

The team has 3 papers accepted at NeurIPS 2020
Winner of the Koenderink test of time paper award at ECCV 2020
The team has 4 papers accepted at ECCV 2020
Grégory Rogez is co-editor of the IJCV Special Issue on Human Pose, Motion, Activities and Shape in 3D
1 paper (oral) accepted at CVPR 2020. See Blog.
Diane Larlus is serving as Area Chair for CVPR 2020 and ECCV 2020
The paper by César de Souza et al. on “Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models“, was accepted for publication in IJCV.
Yannis Kalantidis is organizing the Computer Vision for Agriculture (CV4A) workshop at ICLR 2020.
Diane Larlus is serving as Industrial Liaison Chair for ECCV 2020

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. Our driving goal is to use our research to deliver ambient visual intelligence to our users in autonomous driving, robotics, via phone cameras and any other visual means to reach people wherever they may be.

Our research combines skills in machine learning, pattern recognition and computer vision, and we work on multi-disciplinary problems with teams specialised in natural language processing, user experience, ethnography, design and more. Our research efforts may be either long-term in focus, or may tackle problems with concrete and immediate relevance to NAVER products and services. We’re very active in the computer vision community and our research is often pursued in collaboration with external partners from government and academia.