  Visual tempo characterizes the dynamics and the temporal scale of an action.
Modeling such visual tempos of different actions facilitates their recognition.
Previous works often capture the visual tempo through sampling raw videos at
multiple rates and constructing an input-level frame pyramid, which usually
requires a costly multi-branch network to handle. In this work we propose a
generic Temporal Pyramid Network (TPN) at the feature-level, which can be
flexibly integrated into 2D or 3D backbone networks in a plug-and-play manner.
Two essential components of TPN, the source of features and the fusion of
features, form a feature hierarchy for the backbone so that it can capture
action instances at various tempos. TPN also shows consistent improvements over
other challenging baselines on several action recognition datasets.
Specifically, when equipped with TPN, the 3D ResNet-50 with dense sampling
obtains a 2% gain on the validation set of Kinetics-400. A further analysis
also reveals that TPN gains most of its improvements on action classes that
have large variances in their visual tempos, validating the effectiveness of
TPN.
