We focus on the problem of recognising the end state of an action in an
image, which is critical for understanding what action is performed and in
which manner. We study this focusing on the task of predicting the coarseness
of a cut, i.e., deciding whether an object was cut "coarsely" or "finely". No
dataset with these annotated end states is available, so we propose an
augmentation method to synthesise training data. We apply this method to
cutting actions extracted from an existing action recognition dataset. Our
method is object agnostic, i.e., it presupposes the location of the object but
not its identity. Starting from less than a hundred images of a whole object,
we can generate several thousands images simulating visually diverse cuts of
different coarseness. We use our synthetic data to train a model based on UNet
and test it on real images showing coarsely/finely cut objects. Results
demonstrate that the model successfully recognises the end state of the cutting
action despite the domain gap between training and testing, and that the model
generalises well to unseen objects.

我们研究了在图像中识别动作的结束状态的问题，重点是预测切割的粗细，通过合成训练数据的方法进行数据增广，使用基于 UNet 的模型进行训练，并且成功地识别了切割动作的结束状态，展示了模型在训练和测试之间的领域转化，并且对未知对象具有良好的泛化性。