Visual AI: An Open World Interpretable Visual Transformer

随着深度学习的到来和大数据的可用，现在可以训练机器学习算法来执行多种视觉任务，例如在云中标记个人图像集合、识别人脸和使用手机进行3D形状扫描。然而，目前这些任务中的每一项都需要在专门为该任务收集和标记的非常大的图像数据集上训练神经网络。由此产生的网络是目标任务的优秀专家，但他们只了解在培训期间经历的‘封闭世界’，不能‘说’任何关于其他内容的有用内容，也不能在没有再培训的情况下应用于其他任务，他们也没有能力解释他们的决定或认识到他们的局限性。此外，目前的视觉算法通常是“单一模式”的，他们对其他可能容易获得的模式(音频、文本)“视而不见”。该方案的核心目标是开发没有这些限制的下一代视听算法。我们将进行基础研究，以开发一种能够进行视觉分析的视觉变形金刚，具有人类视觉系统的灵活性和可解释性，并辅之以另一种‘感官’--音频和文本。它将能够不断地从原始数据流中学习，而不需要为每个新任务建立新的数据集，并在多种数据类型(例如，带有音频的视频、超大规模图像和视频数据集以及带有文本记录的医学图像)上传递和提取语义和几何信息。视觉转换器将成为下一代人工智能的关键组件，能够处理多个下游视听任务，显著取代目前计算机视觉系统的局限性，并实现新的和深远的应用。第二个目标是传输和翻译。我们寻求在各种其他学术学科和行业中产生影响，这些学科和行业今天大大低估了最新计算机视觉思想的力量。我们将针对这些学科，使它们能够跨越目前以手动审查和高度交互的逐帧分析为主的使用(或不使用)之间的鸿沟，进入对非常大的数据集进行自动化视觉分析成为常态的新时代。简而言之，我们的目标是确保新开发的方法被其他领域的工业界和学术界研究人员使用，并转化为具有社会效益和经济效益的产品。为此，将在项目网站上传播开源软件、数据集和演示程序。数字图像和视频的无处不在意味着每个英国公民都可能以不同的方式从该计划的研究中受益。一个例子是智能视听眼镜，它可以通过嘴唇的运动来掩盖其他环境声音，从而注意到一个人的谈话。第二款是一款可以回答视觉问题(或检索匹配)的应用程序，用于在大规模视听集合中进行文本查询，比如一个人的整个个人视频。第三种是人工智能引导的医疗筛查，可以帮助受过最低限度培训的医疗专业人员执行医疗扫描。

With the advent of deep learning and the availability of big data, it is now possible to train machine learning algorithms for a multitude of visual tasks, such as tagging personal image collections in the cloud, recognizing faces, and 3D shape scanning with phones. However, each of these tasks currently requires training a neural network on a very large image dataset specifically collected and labelled for that task. The resulting networks are good experts for the target task, but they only understand the 'closed world' experienced during training and can 'say' nothing useful about other content, nor can they be applied to other tasks without retraining, nor do they have an ability to explain their decisions or to recognise their limitations. Furthermore, current visual algorithms are usually 'single modal', they 'close their ears' to the other modalities (audio, text) that may be readily available.The core objective of the Programme is to develop the next generation of audio-visual algorithms that does not have these limitations. We will carry out fundamental research to develop a Visual Transformer capable of visual analysis with the flexibility and interpretability of a human visual system, and aided by the other 'senses' - audio and text. It will be able to continually learn from raw data streams without requiring the traditional 'strong supervision' of a new dataset for each new task, and deliver and distill semantic and geometric information over a multitude of data types (for example, videos with audio, very large scale image and video datasets, and medical images with text records).The Visual Transformer will be a key component of next generation AI, able to address multiple downstream audio-visual tasks, significantly superseding the current limitations of computer vision systems, and enabling new and far reaching applications.A second objective addresses transfer and translation. We seek impact in a variety of other academic disciplines and industry which today greatly under-utilise the power of the latest computer vision ideas. We will target these disciplines to enable them to leapfrog the divide between what they use (or do not use) today which is dominated by manual review and highly interactive analysis frame-by-frame, to a new era where automated visual analytics of very large datasets becomes the norm. In short, our goal is to ensure that the newly developed methods are used by industry and academic researchers in other areas, and turned into products for societal and economic benefit. To this end open source software, datasets, and demonstrators will be disseminated on the project website.The ubiquity of digital images and videos means that every UK citizen may potentially benefit from the Programme research in different ways. One example is smart audio-visual glasses, that can pay attention to a person talking by using their lip movements to mask out other ambient sounds. A second is an app that can answer visual questions (or retrieve matches) for text-queries over large scale audio-visual collections, such as a person's entire personal videos. A third is AI-guided medical screening, that can aid a minimally trained healthcare professional to perform medical scans.