  We introduce the Action Transformer model for recognizing and localizing
human actions in video clips. We repurpose a Transformer-style architecture to
aggregate features from the spatiotemporal context around the person whose
actions we are trying to classify. We show that by using high-resolution,
person-specific, class-agnostic queries, the model spontaneously learns to
track individual people and to pick up on semantic context from the actions of
others. Additionally its attention mechanism learns to emphasize hands and
faces, which are often crucial to discriminate an action - all without explicit
supervision other than boxes and class labels. We train and test our Action
Transformer network on the Atomic Visual Actions (AVA) dataset, outperforming
the state-of-the-art by a significant margin using only raw RGB frames as
input.
