MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matching
Researchers have developed MLT-Dedup, a new framework for efficiently identifying and removing near-duplicate videos from large online platforms. The system uses a Multi-Level Video Encoder to create both detailed frame-level and sparse clip-level embeddings, allowing for fast candidate retrieval and precise matching. A novel Differential Feature-enhanced Similarity Module, DiF-SiM, pinpoints duplicated segments and provides evidence for deduplication decisions. Experiments show MLT-Dedup reduces online video repetition by 91% with 90% precision and increases indexing capacity fivefold. AI
IMPACT Improves efficiency and user experience on video platforms by reducing redundant content.