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

Shear：下一代视频理解技术，可在互联网上自动进行内容审核。

• 批准号: 71653
• 金额: $ 49.16万
• 依托单位: UNITARY LTD
• 依托单位国家: 英国
• 项目类别: Study
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=71653
• 关键词: Shear; next; generation; video; understanding

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.

在这个项目中，Unitary Ltd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Unitary的新产品_Shear_，用于自动检测在线有害视频内容。为了确保速度和准确性，并保护版主的心理健康，迫切需要自动审核。当前的解决方案将每个视频视为一系列帧并应用图像分析。单独分析音频以检测关键字。但是，对时间的任何理解（帧的顺序）或对上下文的意识都丢失了。** 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的视频的一些类型和示例：1.理解 ** 互动 ** 至关重要的视频例如，包含枪的单个帧不一定会泄露这是否涉及现实生活中的大屠杀，计算机游戏或电影场景。不幸的是，描述虐待动物的视频很常见，这需要了解 ** 运动 ** 或时间意识。在一个示例中，看到一只狗紧挨着一个拿着棒球棒的人。蝙蝠摆动，屏幕变暗，听到可怕的嘎吱声。这是一个非常令人不安的视频，但没有一个单独的帧可以提出警报。** 多个 * 信号 ** 必须一起解释 ** 的视频 ** 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，以便角色要求观众（即儿童）做危险的事情，例如“打开烤箱”或玩电线/插座。仅图像本身就显示了熟悉的卡通，音频本身并不值得关注--没有任何亵渎，事实上它可能会被误认为是成人的DIY视频！但是，这一音频内的卡通组合是什么使它不可接受。4\。视觉相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巨魔的叙述一起发布。该项目将产生突破性的技术，可以解释各种信号，以增强对时间和背景的理解，从而提高对上述视频的检测。我们的目标是颠覆适度产业，这是目前非常手动和成熟的创新。