论文标题
内容适中和电影内容等级的深度体系结构
Deep Architectures for Content Moderation and Movie Content Rating
论文作者
论文摘要
根据其内容对视频进行评分是对视频年龄类别进行分类的重要步骤。电影内容评级和电视节目评级是专业委员会建立的两个最常见的评级系统。但是，委员会手动审查和评估场景/电影内容是一项繁琐的工作，随着不断增长的在线视频内容，它变得越来越困难。因此，理想的解决方案是使用基于计算机的视频内容分析技术来自动化评估过程。在本文中，总结了相关作品，以供行动识别，多模式学习，电影类型分类和敏感的内容检测，以介绍内容审核和电影内容等级。该项目页面可在https://github.com/fcakyon/content-moderation-deep-learning上找到。
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.
