  Anticipating human actions is an important task that needs to be addressed
for the development of reliable intelligent agents, such as self-driving cars
or robot assistants. While the ability to make future predictions with high
accuracy is crucial for designing the anticipation approaches, the speed at
which the inference is performed is not less important. Methods that are
accurate but not sufficiently fast would introduce a high latency into the
decision process. Thus, this will increase the reaction time of the underlying
system. This poses a problem for domains such as autonomous driving, where the
reaction time is crucial. In this work, we propose a simple and effective
multi-modal architecture based on temporal convolutions. Our approach stacks a
hierarchy of temporal convolutional layers and does not rely on recurrent
layers to ensure a fast prediction. We further introduce a multi-modal fusion
mechanism that captures the pairwise interactions between RGB, flow, and object
modalities. Results on two large-scale datasets of egocentric videos,
EPIC-Kitchens-55 and EPIC-Kitchens-100, show that our approach achieves
comparable performance to the state-of-the-art approaches while being
significantly faster.
