The 2023 video similarity dataset and challenge

Abstract

This work introduces a dataset, benchmark, and challenge for the problem of video copy tracing. There are two related tasks: determining whether a query video shares content with a reference video (“detection”) and temporally localizing the shared content within each video (“localization”). The benchmark is designed to evaluate methods on these two tasks. It simulates a realistic needle-in-haystack setting, where the majority of both query and reference videos are “distractors” containing no copied content. We propose an accuracy metric for both tasks. The associated challenge imposes computing resource restrictions that reflect real-world settings. We also analyze the results and methods of the top submissions to the challenge. The dataset, baseline methods, and evaluation code are publicly available and were discussed at the Visual Copy Detection Workshop (VCDW) at CVPR’23. We provide reference code for evaluation and baselines at: https://github.com/facebookresearch/vsc2022.