  This work focuses on anticipating long-term human actions, particularly using
short video segments, which can speed up editing workflows through improved
suggestions while fostering creativity by suggesting narratives. To this end,
we imbue a transformer network with a symbolic knowledge graph for action
anticipation in video segments by boosting certain aspects of the transformer's
attention mechanism at run-time. Demonstrated on two benchmark datasets,
Breakfast and 50Salads, our approach outperforms current state-of-the-art
methods for long-term action anticipation using short video context by up to
9%.
