  To help customers make better-informed viewing choices, video-streaming
services try to moderate their content and provide more visibility into which
portions of their movies and TV episodes contain age-appropriate material
(e.g., nudity, sex, violence, or drug-use). Supervised models to localize these
sensitive activities require large amounts of clip-level labeled data which is
hard to obtain, while weakly-supervised models to this end usually do not offer
competitive accuracy. To address this challenge, we propose a novel Coarse2Fine
network designed to make use of readily obtainable video-level weak labels in
conjunction with sparse clip-level labels of age-appropriate activities. Our
model aggregates frame-level predictions to make video-level classifications
and is therefore able to leverage sparse clip-level labels along with
video-level labels. Furthermore, by performing frame-level predictions in a
hierarchical manner, our approach is able to overcome the label-imbalance
problem caused due to the rare-occurrence nature of age-appropriate content. We
present comparative results of our approach using 41,234 movies and TV episodes
(~3 years of video-content) from 521 sub-genres and 250 countries making it by
far the largest-scale empirical analysis of age-appropriate activity
localization in long-form videos ever published. Our approach offers 107.2%
relative mAP improvement (from 5.5% to 11.4%) over existing state-of-the-art
activity-localization approaches.
