Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content. This paper presents our first and second solutions to the Meta AI Video Similarity Challenge (VSC22), CVPR 2023. In order to compete in this challenge, we propose Feature-Compatible Progressive Learning (FCPL) for VCD. FCPL trains various models that produce mutually-compatible features, meaning that the features derived from multiple distinct models can be directly compared with one another. We find this mutual compatibility enables feature ensemble. By implementing progressive learning and utilizing labeled ground truth pairs, we effectively gradually enhance performance. Experimental results demonstrate the superiority of the proposed FCPL over other competitors. Our code is available at https://github.com/WangWenhao0716/VSC-DescriptorTrack-Submission and https://github.com/WangWenhao0716/VSC-MatchingTrack-Submission.

翻译：视频复制检测（VCD）技术旨在识别未经授权或重复的视频内容。本文介绍了我们在CVPR 2023 Meta AI视频相似性挑战赛（VSC22）中提出的第一和第二套解决方案。为应对该挑战，我们提出了面向VCD的特征兼容渐进式学习（FCPL）。FCPL训练能够生成相互兼容特征的多种模型，即多个不同模型提取的特征可直接相互比较。我们发现这种相互兼容性使得特征集成成为可能。通过实施渐进式学习并利用带标注的真实配对数据，我们有效逐步提升了性能。实验结果表明，所提出的FCPL方法优于其他参赛方案。我们的代码已开源至https://github.com/WangWenhao0716/VSC-DescriptorTrack-Submission 和 https://github.com/WangWenhao0716/VSC-MatchingTrack-Submission。