  Video language continual learning involves continuously adapting to
information from video and text inputs, enhancing a model's ability to handle
new tasks while retaining prior knowledge. This field is a relatively
under-explored area, and establishing appropriate datasets is crucial for
facilitating communication and research in this field. In this study, we
present the first dedicated benchmark, ViLCo-Bench, designed to evaluate
continual learning models across a range of video-text tasks. The dataset
comprises ten-minute-long videos and corresponding language queries collected
from publicly available datasets. Additionally, we introduce a novel
memory-efficient framework that incorporates self-supervised learning and
mimics long-term and short-term memory effects. This framework addresses
challenges including memory complexity from long video clips, natural language
complexity from open queries, and text-video misalignment. We posit that
ViLCo-Bench, with greater complexity compared to existing continual learning
benchmarks, would serve as a critical tool for exploring the video-language
domain, extending beyond conventional class-incremental tasks, and addressing
complex and limited annotation issues. The curated data, evaluations, and our
novel method are available at https://github.com/cruiseresearchgroup/ViLCo .
