  Despite great success has been achieved in activity analysis, it still has
many challenges. Most existing work in activity recognition pay more attention
to design efficient architecture or video sampling strategy. However, due to
the property of fine-grained action and long term structure in video, activity
recognition is expected to reason temporal relation between video sequences. In
this paper, we propose an efficient temporal reasoning graph (TRG) to
simultaneously capture the appearance features and temporal relation between
video sequences at multiple time scales. Specifically, we construct learnable
temporal relation graphs to explore temporal relation on the multi-scale range.
Additionally, to facilitate multi-scale temporal relation extraction, we design
a multi-head temporal adjacent matrix to represent multi-kinds of temporal
relations. Eventually, a multi-head temporal relation aggregator is proposed to
extract the semantic meaning of those features convolving through the graphs.
Extensive experiments are performed on widely-used large-scale datasets, such
as Something-Something and Charades, and the results show that our model can
achieve state-of-the-art performance. Further analysis shows that temporal
relation reasoning with our TRG can extract discriminative features for
activity recognition.
