VideoModerator: A Risk-aware Framework for Multimodal Video Moderation in E-Commerce
This addresses the tedious and time-consuming task of video moderation for e-commerce platforms, but it appears incremental as it builds on existing multimodal and visualization techniques.
The authors tackled the problem of moderating multimodal video content in e-commerce livestreams by proposing VideoModerator, a risk-aware framework that integrates machine learning models and interactive visualizations, and they validated its effectiveness through experiments and a user study.
Video moderation, which refers to remove deviant or explicit content from e-commerce livestreams, has become prevalent owing to social and engaging features. However, this task is tedious and time consuming due to the difficulties associated with watching and reviewing multimodal video content, including video frames and audio clips. To ensure effective video moderation, we propose VideoModerator, a risk-aware framework that seamlessly integrates human knowledge with machine insights. This framework incorporates a set of advanced machine learning models to extract the risk-aware features from multimodal video content and discover potentially deviant videos. Moreover, this framework introduces an interactive visualization interface with three views, namely, a video view, a frame view, and an audio view. In the video view, we adopt a segmented timeline and highlight high-risk periods that may contain deviant information. In the frame view, we present a novel visual summarization method that combines risk-aware features and video context to enable quick video navigation. In the audio view, we employ a storyline-based design to provide a multi-faceted overview which can be used to explore audio content. Furthermore, we report the usage of VideoModerator through a case scenario and conduct experiments and a controlled user study to validate its effectiveness.