3rd Place Solution to Meta AI Video Similarity Challenge
This work addresses video copy detection for competition participants, but it is incremental as it builds on existing methods.
The paper tackled video copy detection by adapting existing image copy detection techniques to video data, achieving a 38% improvement in the Descriptor Track and a 60% improvement in the Matching Track compared to baselines.
This paper presents our 3rd place solution in both Descriptor Track and Matching Track of the Meta AI Video Similarity Challenge (VSC2022), a competition aimed at detecting video copies. Our approach builds upon existing image copy detection techniques and incorporates several strategies to exploit on the properties of video data, resulting in a simple yet powerful solution. By employing our proposed method, we achieved substantial improvements in accuracy compared to the baseline results (Descriptor Track: 38% improvement, Matching Track: 60% improvement). Our code is publicly available here: https://github.com/line/Meta-AI-Video-Similarity-Challenge-3rd-Place-Solution