  Video Copy Detection (VCD) has been developed to identify instances of
unauthorized or duplicated video content. This paper presents our second place
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.
