  The aim of this paper is to formalize a new continual semi-supervised
learning (CSSL) paradigm, proposed to the attention of the machine learning
community via the IJCAI 2021 International Workshop on Continual
Semi-Supervised Learning (CSSL-IJCAI), with the aim of raising field awareness
about this problem and mobilizing its effort in this direction. After a formal
definition of continual semi-supervised learning and the appropriate training
and testing protocols, the paper introduces two new benchmarks specifically
designed to assess CSSL on two important computer vision tasks: activity
recognition and crowd counting. We describe the Continual Activity Recognition
(CAR) and Continual Crowd Counting (CCC) challenges built upon those
benchmarks, the baseline models proposed for the challenges, and describe a
simple CSSL baseline which consists in applying batch self-training in temporal
sessions, for a limited number of rounds. The results show that learning from
unlabelled data streams is extremely challenging, and stimulate the search for
methods that can encode the dynamics of the data stream.
