  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.
