  Temporal action localization is an important task of computer vision. Though
a variety of methods have been proposed, it still remains an open question how
to predict the temporal boundaries of action segments precisely. Most works use
segment-level classifiers to select video segments pre-determined by action
proposal or dense sliding windows. However, in order to achieve more precise
action boundaries, a temporal localization system should make dense predictions
at a fine granularity. A newly proposed work exploits
Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the
predictions of 3D ConvNets, making it possible to perform per-frame action
predictions and achieving promising performance in terms of temporal action
localization. However, CDC network loses temporal information partially due to
the temporal downsampling operation. In this paper, we propose an elegant and
powerful Temporal Preservation Convolutional (TPC) Network that equips 3D
ConvNets with TPC filters. TPC network can fully preserve temporal resolution
and downsample the spatial resolution simultaneously, enabling frame-level
granularity action localization. TPC network can be trained in an end-to-end
manner. Experiment results on public datasets show that TPC network achieves
significant improvement on per-frame action prediction and competing results on
segment-level temporal action localization.
