Continual Learning in Computer Vision (CLVision, 3rd Edition)
The CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) aims to gather researchers and engineers from academia and industry to discuss the latest advances in Continual Learning. In this one-day workshop, we will have 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.
Praktische info:
Inschrijven?
- Inschrijvingen: tot 16 feb. 2030
- Prijs: price on request
- Enrolment period:
- Paper submission deadline: 9 maart 2022
- Notification to authors: 28 maart 2022
- Camera ready deadline: 8 april 2022
- Enrolment period:
georganiseerd door:
Leertraject
Topics covered
We are interested in everything that is related to Continual Learning. Topics of the papers include but are not limited to:
- Lifelong / Continual / Incremental / Online learning
- Few-shot learning & Transfer learning
- Benchmarks, Scenarios, evaluation protocols and metrics
- Applications and use-cases of Continual Learning
- Bio-inspired learning (memory, synaptic plasticity, and the like)
- Curiosity
- Multitask learning
Challenge overview
The popularity of the continual learning (CL) field has rapidly increased in recent years. The ability to adapt to new environments and learn new skills will be an essential building block towards the creation of autonomous agents.
Continual learning and computer vision areas form a long-standing research duo. To date, most of the research efforts have been directed towards tackling the object classification problem. The importance of this problem cannot be argued, as it serves as the initial step towards building continuously learning systems for vision applications. However, this doesn’t hold true for the object detection problem.
Another direction of interest is the instance-level object recognition, in which the goal is to predict which specific object is depicted. This is in contrast with category-level recognition, where the goal is to predict the general category objects.
Lesgevers / sprekers
Bing Liu
Bing Liu is a distinguished professor of Computing at the University of Illinois at Chicago (UIC). He received his Ph.D. in Artificial Intelligence (AI) from the University of Edinburgh. Before joining UIC, he was a faculty member at the School of Computing, National University of Singapore (NUS). He was also with Peking University for one year (2019-2020). His research interests include lifelong and continual learning, sentiment analysis, lifelong learning chatbots, open-world AI/learning, natural language processing (NLP), and data mining and machine learning.
Sebastian Risi
I am an Artificial Intelligence researcher that aims to make machines more adaptive and creative. My research is focused on computational evolution, deep learning, and crowdsourcing, with applications in robotics, video games, design, and art. I have recently been awarded an ERC Consolidator grant for my project GROW-AI: Growing Machines Capable of Rapid Learning in Unknown Environments.
I am the director of the Creative AI Lab, and co-direct the Robotics, Evolution and Art Lab (REAL) at the IT University of Copenhagen. I am also a co-founder of modl.ai, a company that develops AI techniques for game development.
Jonathan Schwarz
I'm the Head of AI Research at Thomson Reuters, leading TR's Foundational Research Team. Previously, I served as the Co-Founder and Chief Scientific Officer (CSO) of Safe Sign Technologies (acquired by TR).
Siddharth Swaroop
I am a postdoctoral fellow in the Data to Actionable Knowledge group, working with Prof Finale Doshi-Velez.
I obtained my PhD in the Computational and Biological Learning lab at the University of Cambridge. During my PhD, I designed algorithms for large-scale machine learning systems that learn sequentially without revisiting past data, are privateover user data, and are uncertainty-aware. My PhD was funded by an EPSRC DTP award and a Microsoft Research EMEA PhD Award. I also held an Honorary Vice-Chancellor’s Award from the Cambridge Trust.
Tyler L. Hayes
Hello! I'm a researcher with a keen interest in extending machine learning models beyond their training distributions and applying them across diverse computer vision applications. Most recently I was working as a Research Scientist at NAVER LABS Europe in the French Alps. While there, I helped develop models for novel class discovery and open-vocabulary object detection. I was also a Board Member of the ContinualAI non-profit organization.
Joost van de Weijer
Computer Vision Center, Barcelona
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