易记性：信息实用性的图像可计算量度（CS）

图像中的像素以及它们构成的对象，场景和动作决定了图像是令人印象深刻的还是容易被忘记的。尽管记忆力会随图像而发生变化，但它在很大程度上独立于单个观察者。观察者之间的独立性使记忆力成为可计算图像的信息量度以及符合自动预测的条件。在本章节中，我们使用计算型镜头放大了记忆力，详细介绍了使用从原始像素到语义标签的不同比例的图像特征，它们可以准确地预测与人类行为数据相关的图像记忆力的最新算法。我们还会讨论人脸，物体和场景记忆性的算法和可视化的设计，以及从静态场景扩展到动作和视频的算法。我们将介绍最先进的深度学习方法，这些方法是记忆力预测领域中的当前领跑者。除了这些预测，我们还会展示人工智能的最新发展，这些方法可用于创建和修改视觉记忆力。最后，我们预览了记忆力可以增强的计算应用程序，从过滤视觉流到增强增强现实界面。

原文题目：Memorability: An image-computable measure of information utility

原文：The pixels in an image, and the objects, scenes, and actions that they compose, determine whether an image will be memorable or forgettable. While memorability varies by image, it is largely independent of an individual observer. Observer independence is what makes memorability an image-computable measure of information, and eligible for automatic prediction. In this chapter, we zoom into memorability with a computational lens, detailing the state-of-the-art algorithms that accurately predict image memorability relative to human behavioral data, using image features at different scales from raw pixels to semantic labels. We discuss the design of algorithms and visualizations for face, object, and scene memorability, as well as algorithms that generalize beyond static scenes to actions and videos. We cover the state-of-the-art deep learning approaches that are the current front runners in the memorability prediction space. Beyond prediction, we show how recent A.I. approaches can be used to create and modify visual memorability. Finally, we preview the computational applications that memorability can power, from filtering visual streams to enhancing augmented reality interfaces.