Automated video content moderation is crucial for protecting children from inappropriate content on social media platforms like YouTube. Every minute, over 300 hours of video are uploaded to YouTube, and almost 50% of children have come across inappropriate content. Cartoon videos are particularly problematic as they are attractive to young viewers, and scammers often create junk content with familiar cartoon characters to monetize their videos. Children under 6 years old are more susceptible to viewing harmful content, which can affect their cognitive development.
Prompt Candidate Generation
To address these challenges, researchers generated prompts for vision-language models using cartoon context tokens. They added the token "cartoon" to existing prompts and created new prompt formats like "a clip-token of a context-token." From the initial lists of clip-tokens and context-tokens, they selected top-performing prompts based on zero-shot performance analysis.
Less Well-Defined Video Analysis Problems
Content moderation is challenging as it’s difficult to verbally define what makes a video inappropriate. A famous quote by a Supreme Court Justice highlights the difficulty in defining pornography, and the authors suggest that prompt learning approaches could be used where the prompts are learnable parameters. They also plan to explore fine-tuning on pre-trained video datasets for better performance on cartoon datasets.
Conclusion
In summary, automated video content moderation is critical for protecting children from inappropriate content on social media platforms. Researchers generated prompts for vision-language models using cartoon context tokens to improve content moderation. While content moderation poses significant challenges, prompt learning approaches and fine-tuning pre-trained video datasets could help improve performance on cartoon datasets. By demystifying complex concepts and using everyday language, this summary aims to capture the essence of the article without oversimplifying it.
