Incorporating new knowledge in existing models to adapt to novel problems is a fundamental challenge of computer vision. Humans and animals continuously assimilate new experiences to survive in new environments and to improve in situations already encountered in the past. Moreover, while current computer vision models have to be trained with independent and identically distributed random variables, biological systems incrementally learn from non-stationary data distributions. This ability to learn from continuous streams of data, without interfering with previously acquired knowledge and exhibiting positive transfer is called Continual Learning. The CVPR Workshop on “Continual Learning in Computer Vision” (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 a 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 challenging milestones of AI.