The Potential of Vision-Language Models for Content Moderation of
  Children's Videos
Syed Hammad Ahmed Shengnan Hu Gita Sukthankar
Natural language supervision has been shown to be effective for zero-shot
learning in many computer vision tasks, such as object detection and activity
recognition. However, generating informative prompts can be challenging for
more subtle tasks, such as video content moderation. This can be difficult, as
there are many reasons why a video might be inappropriate, beyond violence and
obscenity. For example, scammers may attempt to create junk content that is
similar to popular educational videos but with no meaningful information. This
paper evaluates the performance of several CLIP variations for content
moderation of children's cartoons in both the supervised and zero-shot setting.
We show that our proposed model (Vanilla CLIP with Projection Layer)
outperforms previous work conducted on the Malicious or Benign (MOB) benchmark
for video content moderation. This paper presents an in depth analysis of how
context-specific language prompts affect content moderation performance. Our
results indicate that it is important to include more context in content
moderation prompts, particularly for cartoon videos as they are not well
represented in the CLIP training data.