A Dual-level Detection Method for Video Copy Detection
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 10 references that are in the map. First posted on arXiv 21 May 2023.
Builds on
9 of 10 references.
References · 9
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,228
- 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 266
- 2022A Self-Supervised Descriptor for Image Copy Detectioncited by 176
- 2018Spreading vectors for similarity searchcited by 118
- 2021The 2021 Image Similarity Dataset and Challengecited by 44
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.