MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identification
Content moderation is critical for online video platforms to ensure content safety, protect creators, and sustain positive user experiences. Beyond filtering harmful content, platforms must guarantee content authenticity at scale so that users are exposed to diverse, original videos rather than low-value reproductions. We present MatchLM2Lite, a real-time, production-grade reproduced content identification (RCI) system that leverages the powerful understanding of a multimodal large language model (MLLM) distilled into a small and fast-inference model. Our system jointly models video, audio, and text signals, operating on pairs of videos to produce fine-grained reproduction scores. The sys...
| Authors | Xiaotian Fan, Hiok Hian Ong, David Yuchen Wang et al. |
|---|---|
| Primary category | cs.MM |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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