Feature-compatible Progressive Learning for Video Copy Detection
This work addresses video copy detection for content moderation and copyright enforcement, representing an incremental improvement in a specific competition setting.
The paper tackles the problem of video copy detection by proposing Feature-Compatible Progressive Learning (FCPL), which trains models to produce mutually-compatible features for direct comparison and ensemble, achieving second place in the Meta AI Video Similarity Challenge (VSC22) at CVPR 2023.
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.