  Continual learning (CL) provides a framework for training models in
ever-evolving environments. Although re-occurrence of previously seen objects
or tasks is common in real-world problems, the concept of repetition in the
data stream is not often considered in standard benchmarks for CL. Unlike with
the rehearsal mechanism in buffer-based strategies, where sample repetition is
controlled by the strategy, repetition in the data stream naturally stems from
the environment. This report provides a summary of the CLVision challenge at
CVPR 2023, which focused on the topic of repetition in class-incremental
learning. The report initially outlines the challenge objective and then
describes three solutions proposed by finalist teams that aim to effectively
exploit the repetition in the stream to learn continually. The experimental
results from the challenge highlight the effectiveness of ensemble-based
solutions that employ multiple versions of similar modules, each trained on
different but overlapping subsets of classes. This report underscores the
transformative potential of taking a different perspective in CL by employing
repetition in the data stream to foster innovative strategy design.
