Feature-compatible Progressive Learning for Video Copy Detection
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 20 Apr 2023.
Builds on
16 of 21 references.
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4 more
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.