论文标题
以自我为中心动作视频数据集的精确负担性注释
Precise Affordance Annotation for Egocentric Action Video Datasets
论文作者
论文摘要
物体负担是人类对象互动中的一个重要概念，它基于人类运动能力和物体的物理特性提供有关行动可能性的信息，从而使任务受益，例如行动预期和机器人模仿学习。但是，现有数据集通常：1）将负担能力与对象功能混合在一起； 2）将负担与目标相关的行动混淆； 3）忽略人类运动能力。本文提出了一个有效的注释方案，通过将目标 - 液化性运动动作和将类型抓住为负担性标签，并引入机械作用的概念来解决这些问题，以表示两个对象之间的动作可能性。我们通过将该方案应用于Epic-Kitchens数据集并通过“负担能力识别”等任务来测试我们的注释，从而提供新的注释。我们定性地验证了接受注释训练的模型可以区分负担和机械行动。
Object affordance is an important concept in human-object interaction, providing information on action possibilities based on human motor capacity and objects' physical property thus benefiting tasks such as action anticipation and robot imitation learning. However, existing datasets often: 1) mix up affordance with object functionality; 2) confuse affordance with goal-related action; and 3) ignore human motor capacity. This paper proposes an efficient annotation scheme to address these issues by combining goal-irrelevant motor actions and grasp types as affordance labels and introducing the concept of mechanical action to represent the action possibilities between two objects. We provide new annotations by applying this scheme to the EPIC-KITCHENS dataset and test our annotation with tasks such as affordance recognition. We qualitatively verify that models trained with our annotation can distinguish affordance and mechanical actions.
