  Human action is naturally compositional: humans can easily recognize and
perform actions with objects that are different from those used in training
demonstrations. In this paper, we study the compositionality of action by
looking into the dynamics of subject-object interactions. We propose a novel
model which can explicitly reason about the geometric relations between
constituent objects and an agent performing an action. To train our model, we
collect dense object box annotations on the Something-Something dataset. We
propose a novel compositional action recognition task where the training
combinations of verbs and nouns do not overlap with the test set. The novel
aspects of our model are applicable to activities with prominent object
interaction dynamics and to objects which can be tracked using state-of-the-art
approaches; for activities without clearly defined spatial object-agent
interactions, we rely on baseline scene-level spatio-temporal representations.
We show the effectiveness of our approach not only on the proposed
compositional action recognition task, but also in a few-shot compositional
setting which requires the model to generalize across both object appearance
and action category.
