  Continual Learning, also known as Lifelong or Incremental Learning, has
recently gained renewed interest among the Artificial Intelligence research
community. Recent research efforts have quickly led to the design of novel
algorithms able to reduce the impact of the catastrophic forgetting phenomenon
in deep neural networks. Due to this surge of interest in the field, many
competitions have been held in recent years, as they are an excellent
opportunity to stimulate research in promising directions. This paper
summarizes the ideas, design choices, rules, and results of the challenge held
at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR
2022. The focus of this competition is the complex continual object detection
task, which is still underexplored in literature compared to classification
tasks. The challenge is based on the challenge version of the novel EgoObjects
dataset, a large-scale egocentric object dataset explicitly designed to
benchmark continual learning algorithms for egocentric category-/instance-level
object understanding, which covers more than 1k unique main objects and 250+
categories in around 100k video frames.
