arXiv:2305.12361
공식 코드 · Papers with Code
A Dual-level Detection Method for Video Copy Detection
원문 초록
With the development of multimedia technology, Video Copy Detection has been a crucial problem for social media platforms. Meta AI hold Video Similarity Challenge on CVPR 2023 to push the technology forward. In this paper, we share our winner solutions on both tracks to help progress in this area. For Descriptor Track, we propose a dual-level detection method with Video Editing Detection (VED) and Frame Scenes Detection (FSD) to tackle the core challenges on Video Copy Detection. Experimental results demonstrate the effectiveness and efficiency of our proposed method. Code is available at https://github.com/FeipengMa6/VSC22-Submission.
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A Similarity Alignment Model for Video Copy Segment Matching
The 2023 Video Similarity Dataset and Challenge
3rd Place Solution to Meta AI Video Similarity Challenge
Feature-compatible Progressive Learning for Video Copy Detection
FiVE: A Fine-grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models
Counteracting temporal attacks in Video Copy Detection
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