Research Paper

Core Innovation

This paper introduces UNIVID, a unified vision-language model that produces policy-aware captions as an interpretable intermediate output for video moderation. It combines expert human-refined and synthetic training data to align with safety guidelines, outperforming fragmented black-box classifiers in accuracy and maintainability.

Why It Matters

Video platforms and social networks face growing challenges in moderating vast amounts of content accurately and transparently. UNIVID reduces violation leakage and over-moderation, enabling more reliable enforcement decisions and lowering engineering complexity. This scalability and interpretability transform moderation workflows for industrial-scale applications.

Potential Customers & Pain Points

• Social media platforms – High false positive and false negative moderation rates
• Video hosting services – Complex policy enforcement and maintenance overhead
• Content moderation vendors – Need for interpretable and scalable moderation tools

Business Model

Enterprise SaaS platform licensing UNIVID API and moderation system integration with tiered pricing based on video volume and feature set.

Competitive Landscape

• Google Video AI
• Microsoft Video Indexer
• Amazon Rekognition Video
• OpenAI multimodal models

Implementation Challenges

• Ensuring continuous policy alignment amid evolving regulations
• Handling diverse video content types and languages
• Integrating with existing moderation infrastructure at scale

Validation Strategy

• Pilot deployments with major social media platforms to measure violation leakage and overkill reduction
• A/B testing against existing moderation pipelines to quantify operational cost savings
• User studies with human moderators to assess interpretability and decision support