CLVISION CVPR 2020: Workshop on Continual Learning in Computer Vision Call for Papers
CLVISION CVPR 2020: Workshop on Continual Learning in Computer Vision
During the past few years we have witnessed a renewed and growing
attention to Continuous Learning (CL). The interest in CL is
essentially twofold. From the artificial intelligence perspective, CL
can be seen as another important step towards the grand goal of
creating autonomous agents which can learn continuously and acquire
new and complex skills and knowledge. From a more practical
perspective, CL looks particularly appealing because it enables two
important properties: adaptability and scalability. One of the key
hallmarks of CL techniques is the ability to update the models by
using only recent data (i.e., without accessing old data). This is
often the only practical solution when learning on the edge from
high-dimensional streaming or ephemeral data, which would be
impossible to keep in memory and process from scratch every time a new
piece of information becomes available. Unfortunately, when (deep or
shallow) neural networks are trained only on new data, they experience
a rapid overriding of their weights with a phenomenon known in the
literature as catastrophic forgetting.
To this end, the goal of the CVPR 2020 Workshop on Continual Learning
(CLVISION) is to explore methods that generalize to a continuous
stream of tasks, incrementally consolidating their knowledge without
interfering with previously learned information. Thus, we encourage
submissions that address the problems of learning from a few examples,
catastrophic forgetting and online learning, large-scale realistic
benchmarks, or bio-inspired systems for continual learning, such as
memory and plasticity. 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 computer vision in continual
learning, arguably one of the most crucial milestones of computer
vision and AI in general.
We solicit paper submissions on novel methods and application
scenarios of Continual Learning.
TOPICS OF INTEREST (include but are not limited to):
Continual/Lifelong learning: Models that are able to adapt to new
tasks without forgetting the previously-learned ones.
Few-shot learning: Models that learn from a few examples.
Transfer learning: Models that use new information to improve the
performance in previous and novel tasks.
Online learning: Models that can learn online.
Bio-inspired learning: Works that take inspiration in nature to
propose fundamental mechanisms for continual learning, such as
memory or synaptic plasticity.
Curiosity: Works where the model identifies the most important
pieces of information to incorporate new knowledge
efficiently. Unsupervised/self-supervised models are welcome.
Metrics: Metrics and benchmarks for continual learning of visual
Experience replay: Experience replay for learning systems and
All accepted papers will be presented as posters. Two papers will be
selected for oral presentation and one paper will be awarded as the
CLVision workshop also provides a comprehensive 2-phase challenge
track to thoroughly assess novel continual learning solutions in the
computer vision context based on 3 different continual learning (CL)
protocols. With this challenge we aim to:
Invite the research community to scale up CL approaches to natural
images and possibly on video benchmarks.
Invite the community to work on solutions that can generalize over
multiple CL protocols and settings (e.g. with or without a
"task" supervised signal).
Provide the first opportunity for comprehensive evaluation on a
shared hardware platform for a fair comparison.
Provide the first opportunity to show the generalization
capabilities (over learning) of the proposed approaches on a
hidden continual learning benchmark.
More details on the CLVision Workshop Challenge can be found here:
The submitted manuscript should follow the CVPR 2019 paper
template. Paper submission through:
The page limit for a full paper is 8 pages (excluding references)
and short-papers is 4-pages (excluding references). We accept
dual submissions to CVPR 2020 and CLVISION 2020, but the
manuscript must contain substantial original contents not
submitted to any other conference, workshop or journal.
Submissions will be rejected without review if they:
contain more than 8 pages (excluding references).
violate the double-blind policy or violate the dual-submission policy.
The accepted papers will be linked at the workshop webpage and
also in the main conference proceedings if the authors agree
Papers will be peer-reviewed under the double-blind policy.
Workshop paper submission deadline: March 20th 2020 (11:59 pm Pacific Time)
Notification to authors: 2nd April 2020
Camera-ready deadline: 10th April 2020 (11:59 pm Pacific Time)
Workshop date: June 14, 2020
Dr Razvan Pascanu, DeepMind.
Prof Chelsea Finn, Assistant Professor at Stanford University.
Prof Cordelia Schmid INRIA Research Director, Head of THOTH Project Team.
Prof David Maltoni, Professor, Universita Di Bologna.
Prof Christopher Kanan, PAIGE, RIT and CornellTech.
Prof Gemma Roig, Ass. Professor at SUTD, MIT.
Subutai Ahmad, VP Research, Numenta.
Pau Rodriguez, Element AI.
German Parisi, University of Hamburg.
David Vazquez, Element AI.
Vincenzo Lomonaco, University of Bologna.
Nikhil Churamani, University of Cambridge.
Zhiyuan (Brett) Chen, Google.
Marc Pickett, Google Research.