Due to the rise in video content creation targeted towards children, there is
a need for robust content moderation schemes for video hosting platforms. A
video that is visually benign may include audio content that is inappropriate
for young children while being impossible to detect with a unimodal content
moderation system. Popular video hosting platforms for children such as YouTube
Kids still publish videos which contain audio content that is not conducive to
a child's healthy behavioral and physical development. A robust classification
of malicious videos requires audio representations in addition to video
features. However, recent content moderation approaches rarely employ
multimodal architectures that explicitly consider non-speech audio cues. To
address this, we present an efficient adaptation of CLIP (Contrastive
Language-Image Pre-training) that can leverage contextual audio cues for
enhanced content moderation. We incorporate 1) the audio modality and 2) prompt
learning, while keeping the backbone modules of each modality frozen. We
conduct our experiments on a multimodal version of the MOB (Malicious or
Benign) dataset in supervised and few-shot settings.

针对面向儿童的视频内容创作数量的增加，需要强大的视频托管平台内容审核方案。我们提出了一种有效的 CLIP 适应方法，利用上下文音频提示来增强内容审核，通过冻结各个模态的背景模块，将音频模态和提示学习融合，对多模态版本的恶意或良性数据集进行了实验。