Enhanced Multimodal Content Moderation of Children's Videos using Audiovisual Fusion
This addresses the need for robust content moderation on platforms like YouTube Kids to protect children from harmful audio content, representing an incremental improvement over existing unimodal methods.
The paper tackles the problem of detecting inappropriate content in children's videos by proposing a multimodal approach that combines audio and visual cues, achieving enhanced content moderation through an adapted CLIP model with prompt learning.
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.