  Recent temporal action proposal generation approaches have suggested
integrating segment- and snippet score-based methodologies to produce proposals
with high recall and accurate boundaries. In this paper, different from such a
hybrid strategy, we focus on the potential of the snippet score-based approach.
Specifically, we propose a new snippet score-based method, named Snippet
Relatedness-based Generator (SRG), with a novel concept of "snippet
relatedness". Snippet relatedness represents which snippets are related to a
specific action instance. To effectively learn this snippet relatedness, we
present "pyramid non-local operations" for locally and globally capturing
long-range dependencies among snippets. By employing these components, SRG
first produces a 2D relatedness score map that enables the generation of
various temporal intervals reliably covering most action instances with high
overlap. Then, SRG evaluates the action confidence scores of these temporal
intervals and refines their boundaries to obtain temporal action proposals. On
THUMOS-14 and ActivityNet-1.3 datasets, SRG outperforms state-of-the-art
methods for temporal action proposal generation. Furthermore, compared to
competing proposal generators, SRG leads to significant improvements in
temporal action detection.
