MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matching
arXiv:2606.12215v1 Announce Type: cross Abstract: The explosive growth of user-generated video content on online platforms is accompanied by the emergence of numerous near-duplicate videos--videos that are identical or highly similar but differ by partial edits. These duplicates degrade user experience and increase storage and bandwidth costs, making large-scale video deduplication a critical task. Existing video deduplication frameworks face a fundamental challenge in retrieving sufficient high-quality candidates under a limited index budget, as well as trade-offs between efficiency and preci
The explosive growth of user-generated video content necessitates more efficient methods for managing near-duplicates, pushing research into advanced video deduplication techniques.
Efficient large-scale video deduplication reduces infrastructure costs for online platforms and improves user experience by minimizing repetitive content.
New multi-level representation and spatial-temporal matching techniques offer a more effective approach to identifying and managing duplicate video content online.
- · Online video platforms
- · Cloud storage providers
- · Content moderation companies
- · Platforms with inefficient storage
- · Users encountering repetitive content
Online platforms can operate more cost-effectively due to reduced storage and bandwidth requirements.
Improved content quality and reduced redundancy could lead to higher user engagement and satisfaction.
The underlying techniques might be adapted for broader content recognition, leading to advances in copyright enforcement or content personalization.
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Read at arXiv cs.LG