  Accurate temporal action proposals play an important role in detecting
actions from untrimmed videos. The existing approaches have difficulties in
capturing global contextual information and simultaneously localizing actions
with different durations. To this end, we propose a Relation-aware pyramid
Network (RapNet) to generate highly accurate temporal action proposals. In
RapNet, a novel relation-aware module is introduced to exploit bi-directional
long-range relations between local features for context distilling. This
embedded module enhances the RapNet in terms of its multi-granularity temporal
proposal generation ability, given predefined anchor boxes. We further
introduce a two-stage adjustment scheme to refine the proposal boundaries and
measure their confidence in containing an action with snippet-level actionness.
Extensive experiments on the challenging ActivityNet and THUMOS14 benchmarks
demonstrate our RapNet generates superior accurate proposals over the existing
state-of-the-art methods.
