CVPR 2023
Workshop on Continual Learning in Computer Vision
Fourth Edition
18 June 2023 | Vancouver, Canada
Location: East 2
Workshop's page on the CVPR virtual conference site: https://cvpr2023.thecvf.com/virtual/2023/workshop/18524 (requires CVPR registration)
Introduction
The CVPR Workshop on Continual Learning (CLVision) aims to gather researchers and engineers from academia and industry to discuss the latest advances in Continual Learning. In this workshop, there are regular paper presentations, invited speakers, and technical benchmark challenges to present the current state of the art, as well as the limitations and future directions for Continual Learning, arguably one of the most crucial milestones of AI.
The 4th edition of the CLVision workshop will take place at CVPR 2023 in Vancouver, Canada.
We invite Continual Learning contributions of any kind, not necessarily related to Computer Vision. Join one of the largest gatherings of Continual Learning in the world!
For the next edition see: https://sites.google.com/view/clvision2024
For the previous edition see: https://sites.google.com/view/clvision2022
Schedule
A more detailed schedule can be found under the tab "Program". All times are local time (PDT).
Invited Speakers
Harbin Institute of Technology, China
Georgia Institute of Technology, USA
University of Oxford, UK
Naver Labs, France
DeepMind Mountain View, USA
MILA & Mc`Gill University, Canada
Participation in the Workshop
Attendance
Attend the workshop in person for an exciting line up of talks, poster presentations and a panel discussion. An in-person registration for CVPR is required for this. For those attending virtually, the talks can be streamed through a Zoom link (which can be found here). For this, you need a virtual registration for CVPR (or an in-person registration). Poster presentations will be in-person only.
We solicit submissions of papers that study the problem of continual learning. We accept papers on a variety of topics, including lifelong learning, few-shot learning, meta learning, incremental learning, online learning, multitask learning, etc. Papers will be peer reviewed under double-blind policy and the submission deadline was 13 March 2023. There is an archival track (with accepted papers published in the proceedings) and a non-archival track (which allows dual submissions). All accepted papers will be presented in poster sessions, with some selected for oral presentations.
The deadline to submit solutions to the pre-selection phase of the challenge track was 20 May 2023.
Contact
Sponsors
