UNIVID: Unified Vision-Language Model for Video Moderation

Researchers introduce UNIVID, a unified vision-language model designed for large-scale video moderation that generates interpretable policy-aware captions instead of opaque classification outputs. The system reduces violation detection errors by 42.7% and false positives by 37.0% while consolidating over 1,000 specialized models into a single backbone, demonstrating practical AI efficiency gains in content moderation infrastructure.

UNIVID addresses a critical infrastructure challenge in content moderation: replacing fragmented, black-box systems with a transparent, multi-modal approach. The model generates human-verifiable captions aligned with safety policies, creating an interpretable intermediate layer between raw video content and enforcement decisions. This architectural shift matters because moderation at global scale requires both accuracy and auditability—decisions must withstand human review and legal scrutiny.

The technical approach combines human-refined labels with synthetic training data to overcome a persistent VLM limitation: standard vision-language models often refuse to process potentially harmful content due to built-in safety guardrails, making them unsuitable for moderation workflows. UNIVID's specialized training recipe solves this by maintaining policy alignment while preserving analytical capability.

The operational impact is substantial. Consolidating 1,000+ policy-specific classifiers into one trainable backbone reduces computational overhead and engineering maintenance costs significantly. The 42.7% reduction in violation leakage and 37.0% reduction in false positives translate directly to better user experience and reduced moderation burden.

This work exemplifies how AI systems designed for specific industrial problems can achieve efficiency gains impossible with general-purpose models. For platform operators managing billions of videos, the shift from specialized classifiers to unified VLM infrastructure represents measurable resource recovery and improved decision transparency. The approach may influence how other platforms rethink content moderation architecture, signaling movement toward more interpretable AI systems at scale.

• →UNIVID consolidates 1,000+ specialized moderation models into a single vision-language backbone, reducing computational and engineering overhead
• →Policy-aware caption generation provides human-verifiable interpretability, addressing the black-box problem in content moderation systems
• →The system achieves 42.7% relative reduction in violation detection errors and 37.0% reduction in false positives over traditional classifiers
• →Specialized training data combining human labels and synthetic data overcomes standard VLM safety-guardrail limitations for moderation tasks
• →Successfully deployed at industrial scale, demonstrating practical feasibility of unified VLMs for cross-functional business applications