Shear: The next generation of video understanding technology to automate content moderation across the internet.

在这个项目中，uni有限公司和牛津大学将开发新的算法来解决视频审核的核心挑战。这项技术将构成unity的新产品——自动检测在线有害视频内容的shear_。自动审核是迫切需要的，以确保速度和准确性，并保护审核员的心理健康。目前的解决方案将每个视频视为一系列帧并应用图像分析。音频被单独分析以检测关键字。但是任何对时间的理解（帧的顺序），或对上下文的意识，都消失了。**视频从根本上比图像携带更多的信息，因此有大量的有害视频，这种方法完全失败。**以下是目前无法用自动化手段检测到的视频的一些类型和示例：在视频中，理解“互动”是必不可少的。，一个包含枪支的单独框架并不一定会泄露这是否涉及现实生活中的大屠杀，电脑游戏还是电影场景。不幸的是，需要了解动物运动或时间意识的视频很常见。在一个例子中，一只狗被看到在一个拿着棒球棒的男人旁边。蝙蝠挥动着，屏幕变暗，然后传来可怕的嘎吱声。这是一段极其令人不安的视频，但没有单独的画面能引起警觉。视频中**多个** **信号**必须**一起解释** *旨在影响和伤害儿童的视频通常包括受欢迎的卡通，这些卡通被操纵，使角色要求观众（即儿童）做危险的事情，例如“打开烤箱”或玩电线/插座。图片本身除了熟悉的卡通之外什么都没有，音频本身也不需要担心——没有亵渎，事实上它可能会被误认为是成人的DIY视频！但是把这种声音和动画片结合在一起是让人无法接受的。视觉上类似的内容可能是有害的，也可能是良性的，这取决于其他因素：例如，裸体肖像可能与女权主义信息或性别歧视喷子的叙述一起发布。该项目将带来突破性的技术，可以解释各种信号，以增强对时间和背景的理解，从而改进对上述视频的检测。我们的目标是颠覆审核行业，这个行业目前非常手工，创新的时机已经成熟。

In this project, Unitary Ltd and Oxford University will develop novel algorithms to address the core challenges of video moderation. This technology will form Unitary's new product, _Shear_, to automatically detect harmful video content online.Automated moderation is desperately needed to ensure both speed and accuracy, and protect moderators' mental health. Current solutions treat each video as a series of frames and apply image analysis. The audio is analysed separately to detect keywords. But any understanding of time (the order of frames), or awareness of context, is lost. **Videos carry fundamentally more information than images, and consequently there is an enormous volume of harmful videos for which this approach completely fails.**Below are some types and examples of videos which are currently impossible to detect with automated means:1. Videos in which understanding **interactions** is essentialE.g., an individual frame containing a gun would not necessarily give away whether this involves a real-life massacre, computer game or movie scene.2\. Videos which require an understanding of **motion** or awareness of timeVideos depicting animal cruelty are unfortunately common. In one example, a dog is seen next to a man holding a baseball bat. The bat swings, the screen goes dark and a horrible crunch is heard. This is an extremely disturbing video, but no individual frame can raise alarm.3\. Videos in which **multiple** **signals** must be interpreted **together**Videos designed to influence and harm children often include popular cartoons which have been manipulated so that the characters ask the audience (i.e. children) to do dangerous things, such as "Turn the oven on" or to play with electric wires/sockets. The images alone show nothing but familiar cartoons, and the audio itself is not cause for concern -- there is no profanity, and in fact it might be mistaken for an adult's DIY video! But the combination of this audio inside a cartoon is what makes it unacceptable.4\. Videos in which **context** is keyVisually similar content can be harmful or benign depending on other factors: e.g. a nude portrait could be posted alongside a feminist message or narration by a sexist troll.This project will result in breakthrough technology that can interpret a variety of signals to enhance understanding of time and context, enabling improved detection of videos such as those described above. We aim to disrupt the moderation industry, one which is currently extremely manual and ripe for innovation.