  Temporal action localization (TAL) in videos is a challenging task,
especially due to the large variation in action temporal scales. Short actions
usually occupy a major proportion in the datasets, but tend to have the lowest
performance. In this paper, we confront the challenge of short actions and
propose a multi-level cross-scale solution dubbed as video self-stitching graph
network (VSGN). We have two key components in VSGN: video self-stitching (VSS)
and cross-scale graph pyramid network (xGPN). In VSS, we focus on a short
period of a video and magnify it along the temporal dimension to obtain a
larger scale. We stitch the original clip and its magnified counterpart in one
input sequence to take advantage of the complementary properties of both
scales. The xGPN component further exploits the cross-scale correlations by a
pyramid of cross-scale graph networks, each containing a hybrid module to
aggregate features from across scales as well as within the same scale. Our
VSGN not only enhances the feature representations, but also generates more
positive anchors for short actions and more short training samples. Experiments
demonstrate that VSGN obviously improves the localization performance of short
actions as well as achieving the state-of-the-art overall performance on
THUMOS-14 and ActivityNet-v1.3.
