Video Content Moderation

Video content moderation studies how to identify and explain risks such as hate speech, child-safety concerns, misinformation, sexual harassment, and noncompliant advertising. Systems combine video transformers and multimodal vision-language models with audio, transcripts, clip-level analysis, policy-guided captions, and evidence-gathering agents; benchmarks test both specific categories and broader harmful-video reasoning. Work also addresses moderation of generated videos, annotation automation, and operational needs such as efficient review and privacy-preserving federated learning. A central challenge is judging context across time and modalities while balancing missed violations against false alarms, and ensuring results generalize across cultures, users, and real-world content.