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.

该研究通过评估多种 CLIP 变种模型在儿童动画视频内容管理中的性能，展示了 Vanilla CLIP with Projection Layer 模型在提供更多上下文环境的内容管理提示方面优于以往工作的结果。研究结果表明，在内容管理提示中加入更多上下文对于卡通视频是必要的，因为它们在 CLIP 训练数据中的表示不足。