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.

中文翻译：

---

将知识添加到无监督算法中以识别意图

在包括对象识别（尤其是细粒度类别）、分割和从 2D 视图重建 3D 对象在内的视觉问题上，计算机视觉算法的性能接近或优于人类。然而，人类能够进行更高级别的图像分析。一个涉及心理理论的明显例子是我们确定感知到的行为或行动是否是有意执行的能力。在本文中，我们推导了一种算法，该算法可以基于其 3D 运动学，利用自推进运动、牛顿运动及其关系的知识来推断场景中代理的行为是有意还是无意。我们展示了这些基础知识的添加如何导致一个简单的、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，从抽象几何动画到代理执行有意和无意动作的真实视频。对这些数据集的实验表明，即使没有训练数据，我们的算法也可以识别一个动作是否是有意的。该性能在定量上与各种监督基线相当，在定性上具有合理的意图分割。