A Dual-level Detection Method for Video Copy Detection
Tianyi Wang, Feipeng Ma, Zhenhua Liu, Fengyun Rao
TL;DR
This paper presents a dual-level detection method combining Video Editing Detection and Frame Scenes Detection to improve video copy detection accuracy and efficiency, winning the CVPR 2023 challenge.
Contribution
It introduces a novel dual-level detection approach specifically designed for video copy detection, advancing the state-of-the-art in this field.
Findings
Effective in detecting copied videos with edits
Efficient and suitable for large-scale applications
Achieved top performance in CVPR 2023 challenge
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Analysis and Summarization · Multimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques