Extraction and Summarization of Explicit Video Content using Multi-Modal Deep Learning
This addresses the need for automated moderation of explicit content on video-sharing platforms, but it appears incremental as it builds on existing multi-modal deep learning approaches.
The authors tackled the problem of automatically detecting explicit content in videos by proposing a multi-modal deep learning pipeline that extracts explicit segments and summarizes them with text for age rating, and they evaluated its effectiveness using standard metrics.
With the increase in video-sharing platforms across the internet, it is difficult for humans to moderate the data for explicit content. Hence, an automated pipeline to scan through video data for explicit content has become the need of the hour. We propose a novel pipeline that uses multi-modal deep learning to first extract the explicit segments of input videos and then summarize their content using text to determine its age appropriateness and age rating. We also evaluate our pipeline's effectiveness in the end using standard metrics.