Deep Architectures for Content Moderation and Movie Content Rating
This work tackles the problem of automating tedious manual video content rating for movie and TV show committees, but it appears incremental as it primarily summarizes related works without presenting new results.
The paper addresses automating video content rating for age classification by summarizing existing computer vision techniques for action recognition, multi-modal learning, movie genre classification, and sensitive content detection, aiming to reduce manual committee work.
Rating a video based on its content is an important step for classifying video age categories. Movie content rating and TV show rating are the two most common rating systems established by professional committees. However, manually reviewing and evaluating scene/film content by a committee is a tedious work and it becomes increasingly difficult with the ever-growing amount of online video content. As such, a desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection in the context of content moderation and movie content rating. The project page is available at https://github.com/fcakyon/content-moderation-deep-learning.