论文标题
将知识添加到无监督的算法中，以识别意图
Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent
论文作者
论文摘要
在视觉问题中，计算机视觉算法的性能在包括对象识别（尤其是细粒类别的类别），分段和3D对象重建中的视觉问题中相近或优于人类。但是，人类具有更高级别的图像分析。一个明显的例子，涉及心理理论，是我们确定是否有意执行感知行为或行动的能力。在本文中，我们得出了一种算法，该算法可以根据自我推测的运动，牛顿运动及其关系来推断场景中的行为是有意还是无意的。我们展示了这种基本知识的添加如何导致一种简单，无监督的算法。为了测试派生算法，我们构建了三个专用数据集，从抽象的几何动画到执行有意和非意外动作的代理的现实视频。这些数据集上的实验表明，即使没有培训数据，我们的算法也可以识别有意的操作。该性能在定量上与各种监督基线相当，具有明智的意向性分割。
Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentation, and 3D object reconstruction from 2D views. Humans are, however, capable of higher-level image analyses. A clear example, involving theory of mind, is our ability to determine whether a perceived behavior or action was performed intentionally or not. In this paper, we derive an algorithm that can infer whether the behavior of an agent in a scene is intentional or unintentional based on its 3D kinematics, using the knowledge of self-propelled motion, Newtonian motion and their relationship. We show how the addition of this basic knowledge leads to a simple, unsupervised algorithm. To test the derived algorithm, we constructed three dedicated datasets from abstract geometric animation to realistic videos of agents performing intentional and non-intentional actions. Experiments on these datasets show that our algorithm can recognize whether an action is intentional or not, even without training data. The performance is comparable to various supervised baselines quantitatively, with sensible intentionality segmentation qualitatively.
