  This work introduces a dataset, benchmark, and challenge for the problem of
video copy detection and localization. The problem comprises two distinct but
related tasks: determining whether a query video shares content with a
reference video ("detection"), and additionally temporally localizing the
shared content within each video ("localization"). The benchmark is designed to
evaluate methods on these two tasks, and simulates a realistic
needle-in-haystack setting, where the majority of both query and reference
videos are "distractors" containing no copied content. We propose a metric that
reflects both detection and localization accuracy. The associated challenge
consists of two corresponding tracks, each with restrictions that reflect
real-world settings. We provide implementation code for evaluation and
baselines. We also analyze the results and methods of the top submissions to
the challenge. The dataset, baseline methods and evaluation code is publicly
available and will be discussed at a dedicated CVPR'23 workshop.
